Method and device for processing online document, computer device and storage medium

Abstract

This application relates to the field of computer technology, and provides a method, apparatus, computer device, and storage medium for processing online documents. The method includes: receiving a request from a target user for accessing an online document; determining a plurality of candidate online documents from online documents within the target user's permissions based on the access request; obtaining the popularity of each candidate online document based on its usage history; wherein the popularity is used to characterize one or a combination of the usage trend and timeliness of the candidate online document; obtaining at least one target online document whose popularity meets a set condition from the plurality of candidate online documents; and responding to the access request by sending display information of the at least one target online document.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a method, apparatus, computer device, and storage medium for processing online documents. Background Technology

[0002] With the continuous development of network technology, online office work has gradually emerged. In online office work, users can share certain documents with other users in the chat window, and other users can view or edit the documents at any time. As users continue to share, the number of documents corresponding to each user will increase, and when a user wants to find a document, they can search for the document they need based on search terms.

[0003] Currently, search results are provided to users by ranking documents based on their relevance to the user's search terms. This method only considers the relevance between documents and search terms, resulting in a limited selection of online documents that differ significantly from what users actually want. In other words, the accuracy of online document searches is currently low. Summary of the Invention

[0004] This application provides a method, apparatus, computer device, and storage medium for processing online documents, which improves the accuracy of obtaining online documents.

[0005] On the one hand, a method for processing online documents is provided, including:

[0006] Receive requests from target users for online documents;

[0007] Based on the acquisition request, multiple candidate online documents are determined from the online documents within the target user's permissions;

[0008] Based on the usage records of each candidate online document, the popularity of each candidate online document is obtained; wherein, the popularity is used to characterize one or a combination of the usage trend and the timeliness of the candidate online document;

[0009] From the plurality of candidate online documents, at least one target online document whose popularity meets the set conditions is obtained;

[0010] In response to the acquisition request, display information of the at least one target online document is sent.

[0011] On the other hand, a method for online document processing is provided, including:

[0012] In response to a target user's targeted action of searching for online documents, a search request is sent.

[0013] Receive and display display information of at least one target online document, and target tag information for each target online document; wherein, the target tag information includes the time attribute and collaboration information of the target online document.

[0014] On the other hand, a method for online document processing is provided, including:

[0015] In response to a target user's action of inserting an online document, a recommendation request is sent.

[0016] Receive and display display information of at least one target online document, and target tag information for each target online document; wherein, the target tag information includes the time attribute and collaboration information of the target online document.

[0017] This application provides an online document processing apparatus, including:

[0018] The send / receive module is used to receive requests from target users for online documents;

[0019] The determination module is used to determine multiple candidate online documents from the online documents within the target user's permissions based on the acquisition request;

[0020] The module is configured to obtain the popularity of each candidate online document based on the usage records of each candidate online document; wherein the popularity is used to characterize one or a combination of the usage trend and the timeliness of the candidate online document; and to obtain at least one target online document whose popularity meets the set conditions from the plurality of candidate online documents.

[0021] The transceiver module is also used to respond to the acquisition request and send the display information of the at least one target online document.

[0022] In one possible embodiment, the transceiver module is further configured to:

[0023] In response to the acquisition request, when sending the display information of the at least one target online document, target tag information of each target online document is also sent, wherein the target tag information includes the time attribute and collaboration information of the target online document.

[0024] In one possible embodiment, the target tag information of the target online document is obtained in the following way:

[0025] Based on the usage records of each target online document, extract key information from one or two of the time attributes and collaboration information of each target online document;

[0026] Based on the key information of each target document, obtain multiple alternative tag information for each target online document;

[0027] For each target online document, the candidate tag information whose relevance meets the relevance threshold among multiple candidate tag information of the target online document is determined as the target tag information of the target online document.

[0028] In one possible embodiment, the obtaining module is specifically used for:

[0029] Get one or any combination of the following information within a set time period: the frequency of access to online documents, the duration of each access, and the time interval between the access time to the candidate online document and the current time.

[0030] Based on the information obtained, the popularity of each candidate online document was determined. The higher the access frequency, the higher the popularity; the longer the access time, the higher the popularity; and the shorter the time interval, the higher the popularity.

[0031] In one possible embodiment, the obtaining module is further specifically used for:

[0032] Obtain the relevance information between the set of group messages associated with the target group within a set time period and the candidate online documents, and

[0033] When determining the popularity of each candidate online document based on the information obtained, the greater the relevance, the higher the popularity.

[0034] In one possible embodiment, the obtaining module is specifically used to obtain the popularity of candidate online documents in any of the following ways:

[0035] Based on access frequency information, the access frequency corresponding to each type of access operation for the candidate online documents is obtained. Then, a weighted sum of the access frequencies for each type of access operation for the candidate online documents is calculated to obtain the popularity of the candidate online documents. The weighting weight for each type of access operation is different; or...

[0036] Based on the duration of each access, the access duration of each user to the candidate online documents is obtained, and the access durations of all users related to the target user are weighted and summed, where users with greater relevance to the target user have larger weights; or,

[0037] Input the time interval between the access time to the candidate online document and the current time into an inverse correlation function to obtain the popularity of the candidate online document.

[0038] In one possible embodiment, the transceiver module is further configured to receive speech information of the target user when viewing the display information of the at least one target online document after sending the display information of the at least one target online document in response to the acquisition request.

[0039] The obtaining module is further configured to parse the sorting criteria in the discourse information, obtain at least one reselected online document that satisfies the sorting criteria, and display the display information of the at least one reselected online document.

[0040] This application provides an online document processing apparatus, including:

[0041] The sending module is used to send a search request in response to a target user's target action of searching for online documents;

[0042] A receiving module is used to receive display information of at least one target online document and target tag information for each target online document; wherein, the target tag information includes the time attribute and collaboration information of the target online document;

[0043] The display module is used to display the presentation information of the at least one target online document, as well as the target tag information of each target online document.

[0044] In one possible embodiment, the sending module is specifically used for:

[0045] The response to the target user's target action of searching for online documents via a webpage is to send a search request; or...

[0046] The system responds to the target user's target action of searching for online documents in the chat window of the instant messaging client by sending a search request.

[0047] In one possible embodiment, the display module is specifically used for:

[0048] The display information of each target online document in the at least one target online document is displayed in descending order of popularity.

[0049] This application provides an online document processing apparatus, including:

[0050] The sending module is used to send a search request in response to a target user's target action of searching for online documents;

[0051] A receiving module is used to receive display information of at least one target online document and target tag information for each target online document; wherein, the target tag information includes the time attribute and collaboration information of the target online document;

[0052] The display module is used to display the presentation information of the at least one target online document, as well as the target tag information of each target online document.

[0053] This application provides a computer device, including:

[0054] At least one processor, and

[0055] A memory that is communicatively connected to the at least one processor;

[0056] The memory stores instructions that can be executed by the at least one processor, and the at least one processor implements any of the online document processing methods by executing the instructions stored in the memory.

[0057] This application provides a storage medium storing computer instructions that, when executed on a computer, cause the computer to perform any of the online document processing methods described above.

[0058] Since the embodiments of this application adopt the above technical solution, they have at least the following technical effects:

[0059] In this embodiment, relevant candidate online documents for the requested document are obtained from the online documents associated with the target user. Based on the usage records of each candidate online document, its popularity is obtained. This popularity reflects, to some extent, the recent usage of online documents by various users, i.e., the timeliness of the online documents. Since each user's usage of online documents more or less influences the target user's access to those documents, the target online documents whose popularity meets the set conditions are more likely to be needed by the user. In other words, the target online documents obtained in this embodiment better meet the target user's usage needs, improving the accuracy of the identified target online documents. Furthermore, since the generated target online documents are candidate online documents whose popularity meets the set conditions, a portion of the candidate online documents can be filtered out, providing the target user with relatively fewer target online documents and avoiding the situation where the user has to sift through a large number of target online documents to find the one they want. Attached Figure Description

[0060] Figure 1 An example image showing search results for online documents provided for related technologies;

[0061] Figure 2 A schematic diagram of the structure of the online document processing system provided in the embodiments of this application;

[0062] Figure 3 A process example diagram illustrating an online document processing method provided in this application embodiment;

[0063] Figure 4 A process example diagram illustrating another online document processing method provided in this application embodiment;

[0064] Figure 5 This application provides a schematic diagram of the interaction process between various devices in its embodiments. Figure 1 ;

[0065] Figure 6 An example diagram of a search interface presented on a webpage, provided as an embodiment of this application;

[0066] Figure 7 An example interface diagram of a search process provided in this application embodiment;

[0067] Figure 8 This application provides a collection of group messages displayed in a chat window within a set time period, as exemplified in this application.

[0068] Figure 9 The embodiments provided in this application are related to Figure 6 The display interface of the corresponding target online document;

[0069] Figure 10 The embodiments provided in this application are related to Figure 7 The corresponding online document search feedback interface;

[0070] Figure 11 This application provides a schematic diagram of the interaction process between various devices in its embodiments. Figure 2 ;

[0071] Figure 12 Example diagrams for displaying target online documents provided in embodiments of this application;

[0072] Figure 13 A schematic diagram of the structure of the online document processing apparatus provided in the embodiments of this application. Figure 1 ;

[0073] Figure 14 A schematic diagram of the structure of the online document processing apparatus provided in the embodiments of this application. Figure 2 ;

[0074] Figure 15 A schematic diagram of the structure of the online document processing apparatus provided in the embodiments of this application. Figure 3 ;

[0075] Figure 16 A schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0076] To better understand the technical solutions provided in the embodiments of this application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0077] To facilitate a better understanding of the technical solutions in the embodiments of this application by those skilled in the art, the terms involved in the embodiments of this application are introduced below.

[0078] Online documents refer to documents published through online channels. Online documents support collaborative processing by multiple users, such as simultaneous collaboration by multiple people. Online documents can be in various formats, such as Word, Excel, TXT, or other formats.

[0079] Usage records refer to the records generated by various users accessing online documents within the same shared space. Usage includes one or more of the following: browsing, viewing, downloading, editing, commenting, and sharing online documents. The same shared space could be members of the same company or members of the same group. Usage records include the online document's time attributes and collaboration information. Specifically, this can include one or more of the following: frequency of access, type of each access, duration of each access, and the time interval between each access and the current time. For example, if user A edits an online document, the backend can record user A's usage record for this instance.

[0080] Target users: refers to users who need to access the target online document.

[0081] The time attributes of online documents refer to the time information related to the online document, including the creation time of the online document, such as the time it was created 2 minutes ago; the viewing time of the online document, such as the time it was viewed by 3 people today; the editing time of the online document, such as the time it was edited by 7 people today; and the last update time of the online document, such as the time it was last updated 30 days ago or the time it was last updated 7 days ago.

[0082] Collaboration information for online documents includes information on collaboration between the target user and other users. Specifically, it includes: the target user's browsing history (e.g., recent browsing); periodic access patterns (e.g., frequent browsing on Mondays, editing on the 5th of each month); and whether the target user received the document in groups (e.g., receiving the document in 3 groups). Collaboration information for other users, such as those within the same organizational structure, includes their collaboration activities (e.g., 6 people in the design department viewed the document today, 30 people within the company viewed it today); and whether organizational members sent the document in conversations (e.g., 2 people in this department sent it to groups).

[0083] Target group: refers to a session group created in an instant messaging client. Each target group has a corresponding group identifier.

[0084] Online documents within the target user's permissions: This refers to online documents that the target user has access to, including those created or published by the target user, as well as those shared or forwarded to the target user by other users. For example, if the target user searches for online documents through a group, then the online documents within the target user's permissions are all documents in the shared space corresponding to that group.

[0085] Popularity: Obtained based on the usage history of online documents. Popularity can be used to characterize the timeliness of online documents, or to characterize the usage trend of online documents, or to characterize both usage trend and timeliness. Timeliness includes the effectiveness of online documents in terms of time and the effectiveness of online documents in terms of content. For example, online documents that have been edited recently are more timeliness than those that have not been edited recently. Usage trend can be understood as the trend of target users using online documents. For example, if users in the same shared space as the target user use an online document more frequently than another online document within a set time period, then it indicates that the usage trend of that online document is relatively better.

[0086] Online content display information: This refers to the target online document and may include one or more of the following combinations: online document identifier, online document link, online document name, online document icon, online document publisher, and online document publication time. An example of an online document icon is the Word icon.

[0087] Hot documents, also known as popular online documents, refer to documents that have recently been viewed or edited by users. This includes documents viewed or edited by users in the same shared space, such as documents viewed or edited by the same user's colleagues. Hot documents can be understood as online documents with high popularity.

[0088] Cold documents, also known as cold online documents, refer to online documents that are outdated and rarely accessed again, such as project progress documents for completed projects. Cold documents can be understood as online documents with relatively low popularity.

[0089] First-result satisfaction rate: This refers to the probability that the first result in a search or recommendation satisfies the user's search intent.

[0090] Coarse ranking: Coarse ranking refers to the process of retrieving relevant results from the database during a search.

[0091] Fine ranking: Fine ranking is the process of returning the top-N ranking results to users after deduplication by the search engine and sorting them according to their weights.

[0092] Cumulative Gain (NDCG): In recommender systems, this represents the sum of the relevance scores of each recommendation result as the score for the entire recommendation list.

[0093] Terminal equipment, also known as a terminal, can be a mobile terminal, fixed terminal, or portable terminal, such as mobile phones, sites, units, devices, multimedia computers, multimedia tablets, internet nodes, communicators, desktop computers, laptop computers, notebook computers, netbook computers, tablet computers, personal communication system (PCS) devices, personal navigation devices, personal digital assistants (PDAs), audio / video players, digital cameras / camcorders, positioning devices, television receivers, radio receivers, e-book devices, gaming devices, or any combination thereof, including accessories, peripherals, or any combination thereof. It is also foreseeable that terminal equipment can support any type of user-facing interface device (e.g., wearable devices).

[0094] Server: A server can be one or more servers. A server can also be a physical server or a virtual server, etc. A server can be a standalone physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms.

[0095] With the increasing prevalence of online document technology, users are receiving more and more online documents. Sometimes, users may want to find previously shared documents, such as those shared by other users in a shared space. The search mechanism in related technologies involves finding documents most relevant to the user's search terms, specifically, finding documents whose names are most closely related to the user's search terms. This search mechanism only considers the relevance between the online document and the search terms, and the search results may not be what the user wants, meaning the accuracy of online document searches is low.

[0096] For example, please refer to Figure 1 Figure 1 shows an example of online document search results in related technologies. The user enters the search term "meeting minutes" as shown in input box 110 in Figure 1. Based on this search term, the device provides the user with search results, including, for example... Figure 1 The example shows multiple online documents named "Meeting Minutes." After obtaining these search results, users still need to open the content of each "Meeting Minutes" document to see if it matches their needs. This demonstrates that the online document search mechanism in this technology does not accurately reflect the user's actual requirements, resulting in low search accuracy. Furthermore, this search mechanism generates a large number of similar documents, undoubtedly increasing the difficulty for users to find the desired results from multiple search terms.

[0097] In view of this, this application provides a method for processing online documents. The technical idea of ​​this method is as follows: Based on a request, candidate online documents are obtained. These candidate online documents are then processed based on their usage records, which include the target user's own usage records and the usage records of other users in the same shared space. The higher the popularity of other users' use of an online document, the greater the likelihood that the target user will obtain that online document. In other words, the target online documents obtained through this method are more in line with the target user's needs, thereby improving the accuracy of the identified target online documents. Furthermore, when obtaining target online documents using popularity, this method can filter out some candidate online documents related to the request, resulting in a relatively smaller number of target online documents provided to the target user later, saving the target user time from further filtering of these target online documents to find the online documents they need.

[0098] In this embodiment, the popularity of an online document can be determined by obtaining one or more combinations of access frequency information, time length information, and the time interval between the access time and the current time based on the usage records of the online document. That is, the popularity is generated based on the usage records of online documents by various users under the same sharing. Each user under the same sharing space has an influence on whether the target user accesses the online document. For example, if a user's associated users access a certain online document, the likelihood of that user accessing the online document is higher. In this way, the degree of match between the target online document determined by the popularity and the online document that the user wants is higher, thereby improving the accuracy of obtaining online documents.

[0099] In this embodiment of the application, when obtaining a target online document, the target tag information of the target online document can be obtained. Even if multiple target online documents with similar names are obtained, the user can filter the target online document they want from the target online documents according to the target tag information.

[0100] The application scenarios of the online document processing method in this application embodiment are described below.

[0101] This method can be used in any scenario involving obtaining online documents, such as searching for online documents or recommending online documents to users. Please refer to [link / reference]. Figure 2 This is a schematic diagram of the structure of an online document processing system. The system includes: a publishing terminal 211, a user terminal 212, an acquisition terminal 213, a processing server 220, and a database 230. Each terminal can communicate with the processing server 220 via a network, such as a local area network (LAN), a wide area network (WAN), the Internet, or a combination thereof.

[0102] Publishing terminal 211 generally refers to a terminal used for publishing online documents. For example, a publisher can access an online document publishing service via a webpage to publish an online document, or they can publish an online document through a client installed on the publishing terminal 211. This client could be a client pre-installed on the terminal or a client embedded in a third-party application. After a publisher publishes an online document through the publishing terminal 211, the processing server 220 can share the online document with other users in the same shared space. Other users in the same shared space, as well as the publisher themselves, can access the online document. The user who publishes the online document can be called the creator.

[0103] Additionally, the processing server 220 can also store the online document in the database 230. When storing an online document, the processing server 220 can store the document content, or it can store the online document identifier and the document content in association. The online document identifier can be one or more combinations of the online document's name, publisher, publication time, etc., or it can be a unique identifier for each online document generated by the processing server 220.

[0104] For example, a publisher can publish an online document through a group on an instant messaging client in publishing terminal 211, and all members of that group can access the online document. When publishing terminal 211 shares an online document through a chat window associated with a group or a contact in the client, the online document is associated with a corresponding group identifier or contact identifier. When processing server 220 stores the online document, it can store the online document in the shared space corresponding to the group identifier or the shared space corresponding to the contact identifier.

[0105] Terminal 212 generally refers to the terminal that accesses an online document after it has been published by the publisher. After a publisher performs a publishing operation on an online document, that publisher can also access the published document; in this case, the publishing terminal 211 associated with that publisher can also function as a user terminal 212. After each user accesses the online document, the user terminal 212 generates a usage record based on the user's access operation. The user terminal 212 then sends this usage record back to the processing server 220, which stores the usage record in the database 230.

[0106] The term "acquiring terminal 213" generally refers to a terminal that requests online documents from the processing server 220. After the processing server 220 stores the online documents and their corresponding usage records, the acquiring terminal 213 can request the target online document from the processing server 220. Acquiring online documents includes many specific scenarios. The following example illustrates the application scenarios of the online document processing method in this embodiment: the acquiring terminal 213 requests a search for online documents, or the acquiring terminal 213 retrieves recommended online documents.

[0107] The first possible scenario is searching online documents:

[0108] For example, terminal 213 receives a search request in response to a user's search operation via the suite; this search request is one example of an acquisition request. Please refer to [link / reference] for details. Figure 3As shown in Figure (1), this is an example of a browser interface. When a user enters a search keyword in the input box 310 of the browser's corresponding webpage, the acquisition terminal 213 generates a search request based on the search keyword. This search request is used to request an online document corresponding to the search keyword, and the acquisition request carries the search keyword. The webpage corresponding to the browser is a web page formatted using Hypertext Markup Language (HTML), which can contain text, images, multimedia content, and programming elements (such as scripts). The content presented on this webpage is maintained by the corresponding staff in the background. This webpage is associated with the processing server 220, and users can upload or create their own online documents through this webpage.

[0109] Or, for example, receiving the response from terminal 213 to the user's target operation of searching online documents through the client, and generating a search request. Please refer to [the relevant documentation] for details. Figure 3 Figure (2) shows an example diagram of an online document search interface associated with a client. Users can enter search keywords in input box 320, and terminal 213 responds to the user's input operation and generates a search request. For example, if a user searches through a group in an instant messaging client, the search request may include not only the search keywords but also the group identifier, which is used to limit the search to online documents from the shared space associated with the group.

[0110] The processing server 220 obtains the target online document according to the online document processing method in this embodiment, and sends the display information of the target online document to the acquisition terminal 213. The specific process of the online document processing method will be described in detail below, and will not be described here.

[0111] The processing server 220 can be a single server or multiple servers. In one possible embodiment, when the processing server 220 obtains the display information of the target online document, it can be achieved through a first server 221, a second server 222, and a third server 223. The first server 221, second server 222, and third server 223 can essentially be understood as three services or three servers. The first server 221 can be the backend server corresponding to the client or the backend server corresponding to the webpage. Specifically, the first server 221 can be a server with a Common Gateway Interface (CGI), or simply a CGI server. CGI is a specification for external programs running on a web server; programs written according to CGI can extend server functionality. CGI applications can interact with browsers and can also communicate with external data sources such as databases through data APIs to retrieve data from databases. The second server 222 can be a coarse-ranking server, used to obtain candidate online documents associated with the search request. The third server 223 can be a fine-ranking server, used to obtain the target online document from the candidate online documents according to popularity.

[0112] For the second possible scenario, online documentation is recommended:

[0113] In some cases, users need the system to recommend online documents, for example, please refer to... Figure 4 This diagram illustrates a scenario for recommending online documents. When a user is editing certain files and needs to insert an online document, the acquisition terminal 213 can generate a recommendation request based on the user's target operation of inserting the online document. This recommendation request is an example of an acquisition request and is used to request the processing server 220 to recommend online documents. After receiving the recommendation request, the processing server 220 obtains the target online document according to the online document processing method in this embodiment and feeds back the target online document as a recommendation result to the acquisition terminal 213.

[0114] It should be noted that the above two scenarios are merely examples of the online document processing method in the embodiments of this application, and the application scenarios of this method are not limited to these.

[0115] Based on the above Figure 3 The application scenarios discussed below will be used to describe the online document processing methods involved in the embodiments of this application.

[0116] Please refer to Figure 5 This represents a schematic diagram of the interaction between the devices, and the specific interaction process includes:

[0117] S501, terminal 213 generates a search request based on the target user's target operation.

[0118] like Figure 3 The discussion covers user search operations on web pages within browsers, such as entering search keywords, which constitutes a target operation. It also includes target operations performed by the user on a client-side interface, such as entering a search query within the target group's application interface. Terminal 213 can respond to these target operations by generating a search request. This search request is used to request online documents related to the search keywords.

[0119] The search request may include the search keywords. The search request may also include the terminal identifier of the acquiring terminal 213, so that search results can be subsequently fed back to the acquiring terminal 213. To facilitate the subsequent processing server 220 in determining the search scope of online documents, if the user sends the search request through a group or contact in an instant messaging client, the search request may also include the group identifier or contact identifier.

[0120] For example, please refer to Figure 6 This is a search interface presented on a webpage. The acquisition terminal 213 can respond to the user's input of search keywords in the input box 601 on the search interface. For example, if the user inputs "meeting minutes", the acquisition terminal 213 will generate a search request based on the input and the search keywords.

[0121] Or, for example, please refer to Figure 7 This diagram illustrates an example of a search process performed on a client-side application, where terminal 213 receives a response for a given task. Figure 7 Clicking on the chat window shown in (1) will display the following: Figure 7 As shown in Figure (2), in response to the user's click on the online file icon 701 in the group information interface, the terminal 213 displays the following: Figure 7 The search interface shown in (3) is obtained. Terminal 213 can respond to the user's request in this... Figure 7 (3) When a user inputs a keyword on the search interface, such as “meeting minutes”, the terminal 213 will generate a search request based on the input operation, the group identifier of the group, and the search keyword.

[0122] S502, terminal 213 sends a search request to first server 221.

[0123] S503, the first server 221 sends the search request to the second server 222.

[0124] S504, the second server 222 sends the document identifier of the alternative online document to the first server 221.

[0125] Terminal 213 will periodically acquire online documents. Upon acquiring a corresponding online document, the first server 221 can generate an online document identifier and keywords for that document. The first server 221 stores the online document identifier and keywords in database 230. The online document identifier can be the name and publisher of the online document, or a unique identifier generated using an encryption algorithm based on the online document's name and other information.

[0126] After receiving the search request, the second server 222 can filter online documents related to the search request from the online documents relevant to the target user. Online documents relevant to the target user include those related to the location where the target user is searching. A specific example is provided below:

[0127] If a user searches through the corresponding page in their browser, the online documents relevant to the target user include those created or published by the user, as well as those received from other users.

[0128] If a user searches through a search interface associated with a specific contact, then online documents relevant to the target user include those related to that contact's chat interface, such as online documents associated with that contact and those shared by that contact within that chat window. If a user searches through a search interface associated with a group, then online documents relevant to the target user include those related to that group, such as online documents shared by the target user through that contact's corresponding chat window, and online documents shared by other contacts in that group through that chat window.

[0129] The second server 222 obtains keywords from online documents, such as by extracting one or more keywords from the online document's name or content. After receiving a search request, the second server 222 extracts the search keywords from the search request and matches these keywords with the keywords of online documents within the search range in database 230. Matched online documents are designated as candidate online documents, which can be the recall results. For example, the second server 222 can identify online documents whose keywords include the search keywords as candidate online documents, or it can determine the similarity between the search keywords and each keyword in the online document. If one or more keywords in the online document have a similarity to the search keywords greater than or equal to a similarity threshold, the second server 222 designates that online document as a candidate online document. The number of candidate online documents can be two or more. The similarity threshold between two words can be determined by performing one-hot encoding on each word and determining the similarity between the encoded sequences.

[0130] If the user enters a unique search keyword, the number of online documents to be selected may be one or none. In this case, since the number of online documents to be selected in the first screening is relatively small, there is no need to perform subsequent steps. The result of the recall is zero or one and is fed back to the first server 221. After receiving the result, the first server 221 feeds it back to the acquisition terminal 213.

[0131] After obtaining the online document identifier of the candidate online document, the second server 222 can send the online document identifier of the candidate online document to the first server 221, so that the first server 221 can obtain the search results of the second server 222.

[0132] S505, query usage records for server 221.

[0133] S506, the first server 221 retrieves usage records from database 230.

[0134] The first server 221 can access the database 230 at any time. After obtaining the online document identifier of the candidate online document, the first server 221 can retrieve the usage records of each candidate online document from the database 230. The usage records can be referred to the content discussed above, and will not be repeated here.

[0135] As one embodiment, steps S505 to S506 are optional. For example, if the first server 221 stores the usage records of each online document, then it is not necessary to retrieve the usage records from the database 230.

[0136] S507, the first server 221 sends the online document identifier and usage record of the candidate online documents to the third server 223.

[0137] After obtaining the recall results from the second server 222, the first server 221 can send the online document identifier and usage record of each online document to the third server 223, so that the third server 223 can further filter the recall results.

[0138] As one embodiment, S506 is an optional step. For example, the first server 221 can send the online document identifiers of each candidate online document to the third server 223, and the third server 223 can access the database 230 to obtain the usage records of the candidate online documents.

[0139] S508, the third server 223 obtains the popularity of each candidate online document based on the usage records of each candidate online document.

[0140] There are several ways to obtain popularity data for the third server 223. The following is an example of how to obtain the popularity data of each alternative online document.

[0141] Method 1:

[0142] The popularity of candidate online documents is determined based on the frequency of access to online documents within a set time period.

[0143] The set time period can be a period of time with a preset duration relative to the current time, or a specified other time period. For example, if the current time is 9:00 AM on May 3, 2020, then the set time period can be from 9:00 AM on May 1, 2020 to 9:00 AM on May 3, 2020.

[0144] Users who access candidate online documents are considered to have access permissions, including the target user and other users. For example, if a user sends a search request within a group, users with access permissions could include all members of that group. Since users with access permissions are more or less related to the target user, such as being in the same group, the higher the frequency of these users accessing the candidate online documents, the greater the likelihood that the target user will also access the document. Therefore, in this embodiment, usage records can be used to obtain access frequency information for candidate online documents within a set time period, and the popularity of the candidate online documents can be determined based on this access frequency information. The following example illustrates how to determine popularity based on access frequency information:

[0145] A1: The access frequency of the candidate online documents is used as the popularity of the candidate online documents.

[0146] Based on usage records, the access frequency information for each candidate online document is obtained. This access frequency information includes the total number of times the online document has been accessed. The third server 223 can directly determine the access frequency of a candidate online document as its popularity; the higher the access frequency, the higher the popularity of the candidate online document. Unless otherwise specified, the access frequency of an online document refers to the total number of times all users access the candidate online document.

[0147] For example, if online document A has a total access frequency of 200 times and online document B has a total access frequency of 100 times, then the popularity of online document A is 200 and the popularity of online document B is 100, and the popularity of online document A is greater than that of online document B.

[0148] A2: The access frequency of various access operations is weighted and summed to obtain the popularity of candidate online documents; among them, different types of access operations have different weights.

[0149] When users access candidate online documents, the specific access operation type of each user for the candidate online documents can be recorded, such as viewing, browsing, downloading, and editing. The third server 223 can count the access frequency under different access operation types, and weight the access frequency under different access operation types. Then, the weighted results under all access operation types are summed to obtain the popularity of the online documents.

[0150] As one example, the longer the access operation takes, the greater the weighting of that type of access operation. The average duration of an access operation can be understood as the average time spent accessing candidate online documents. For example, it can be achieved by counting the time taken by multiple users to perform the access operation and using the average of the access operation times of multiple users as the total operation duration. For instance, the weighting of an access operation is proportional to the average duration of the access operation.

[0151] For example, server 223 counts the number of views for candidate online document A as 100, the number of views as 50, the number of views as 10, and the number of views as 50. The viewing time < the viewing time < the download time < the editing time. Therefore, the popularity of candidate online document A is 50*0.1 + 100*0.2 + 10*0.4 + 5*0.3 = 44.

[0152] In this embodiment, the longer the operation time, the longer the time it takes for a user to access the candidate online document. The longer the time each user takes to perform the operation on the candidate online document, the greater the likelihood that the target user will operate on the online document. Therefore, the popularity can be based on the weighted summation result, which can better predict the likelihood of the target user accessing the online document.

[0153] Method 2:

[0154] The popularity of candidate online documents is determined based on the duration of each access to the online document within a set time period.

[0155] The time period setting can be referenced from the previous discussion and will not be repeated here. After obtaining the usage records, the third server 223 can obtain the duration information of each online document access. The duration information includes the required time for this access, such as the duration of viewing the online document. Based on the duration information of each access, the popularity of candidate online documents can be determined. There are several ways to determine the popularity of candidate online documents based on the duration information. Examples are given below:

[0156] B1: The total duration of each access session is used to determine the popularity of the candidate online documents.

[0157] The third server 223 can obtain the total duration of each user's selected online documents based on the duration of each access to the selected online documents, and use the total duration of the selected online documents as the popularity of the selected online documents.

[0158] For example, if the total duration of online document A is 60 minutes and the total duration of online document B is 40 minutes, then the popularity of online document A is 60 and the popularity of online document B is 40, and the popularity of online document A is greater than that of online document B.

[0159] B2: Weight the duration of each user's access to the candidate online documents to obtain the popularity of the candidate online documents. The weighting of the duration varies for different users.

[0160] The third server 223 determines the duration of each user's access to the candidate online documents based on their usage records, and then weights the durations for different users. Users with a higher relevance to the target user receive a greater weight for their corresponding duration. This relevance can be determined by factors such as the frequency of messages exchanged between the target user and other users. A higher relevance indicates a greater probability that the target user will follow suit and access similar online documents, thus resulting in a greater weight for that user and a more relevant match between the target user and the popularity of the candidate online documents.

[0161] Method 3:

[0162] The popularity of the candidate online documents is determined based on the time interval between the time of access to the candidate online documents and the current time.

[0163] The time interval information can include the time between the most recent access to the candidate online document and the current time. Based on this time interval, the popularity of the candidate online document is determined. The smaller the time interval, the closer the time of access to the candidate online document is to the current time, indicating a higher probability that the candidate online document is likely to be accessed soon, and therefore a more accurate representation of the candidate online document's popularity. Specifically, the time interval can be input into an inverse correlation function to obtain the popularity of the candidate online document. An inverse correlation function generally refers to a function where the independent variable and the dependent variable are inversely correlated, such as an inverse proportional function.

[0164] Method 4: The popularity of an online document can be obtained based on two or three of the following information: access frequency, duration of each access, and time interval between the access time of the candidate online document and the current time.

[0165] C1: The third server 223 can weight two or three of the following factors: access frequency, duration, and the time interval between the access time to the candidate online document and the current time to obtain the popularity of the candidate online document.

[0166] For example, the two types of information include access frequency information and the duration of each access. The third server 223 can weight the access frequency information and the duration of each access, and use the weighted result as the popularity of the candidate online documents.

[0167] For example, the three pieces of information include access frequency, the duration of each access, and the time interval between the access time to the candidate online document and the current time. The third server 223 can weight these three pieces of information—access frequency, duration of each access, and the time interval between the access time to the candidate online document and the current time—and use the weighted result as the popularity of the candidate online document. It should be noted that when weighting these three pieces of information, the weight corresponding to the time interval between the access time to the candidate online document and the current time can be a negative number.

[0168] C2: The third server 223 will use two or three of the following input popularity prediction models to obtain the popularity of the candidate online documents: access frequency, duration, and time interval between accessing the candidate online documents and the current time.

[0169] Taking the frequency of access to an online document and its duration as examples to obtain popularity, the method of determining popularity involved in this application embodiment will be explained as follows:

[0170] Popularity prediction models are used to predict the popularity of candidate online documents. These models can employ machine learning techniques and are trained on sample data, including the popularity tags, access frequency, and duration of each online document. The access frequency and duration of each document are combined to form a feature vector. For example, this could be a combination of the access frequency and duration of each user across all users, or a combination of the sum of access frequencies and the total duration of all users.

[0171] The feature vectors from the sample data are input into the popularity prediction model. The popularity prediction model outputs popularity. The model parameters are adjusted based on the error between the predicted popularity and the popularity label until the error between the predicted popularity and the popularity label output by the popularity prediction model reaches the error condition, thus obtaining the trained popularity prediction model.

[0172] When it is necessary to obtain the popularity of candidate online documents, the access frequency and duration of candidate online documents can be combined into a feature vector. The combination method can refer to the combination method of sample data, which will not be repeated here. The combined feature vector is then input into the prediction popularity model to obtain the popularity of the candidate online document.

[0173] The above example illustrates how to determine popularity based on the frequency and duration of access to online documents. Methods for determining popularity based on the duration of access to online documents and the time interval between accessing candidate online documents and the current time are similarly described above and will not be repeated here.

[0174] Method 5: Combine one or more of the following information: frequency of access to online documents within a set time period, duration of each access, and time interval between the access time to the candidate online documents and the current time, as well as the relevance information between the group message set associated with the target group and the candidate online documents within the set time period, to determine the popularity of the target online document.

[0175] When a target user makes a search request through a target group, the popularity of the target online document may also be related to the group message set of the target group. For example, if the online document is the most discussed in the group message set, then the target user is more likely to search for the target online document. Therefore, in this embodiment, the popularity of the target online document can also be determined by combining the relevance information between the group message set associated with the target group within a set time period and the candidate online documents.

[0176] The relevance information between the set of group messages associated with each other within a set time period and the candidate online documents includes the relevance between the set of group messages and the candidate online documents. This involves the specific method of obtaining the relevance; for example, the third server 223 can retrieve the group messages within the set time period from the database 230 and determine their relevance to the target online document based on the retrieved group messages.

[0177] For example, the number of group messages containing the name of the target online document divided by the total number of group messages within a set time period can be used as the relevance information between the set of group messages associated with the target online document within the set time period and the candidate online document. Similarly, the number of group messages containing keywords of the target online document divided by the total number of group messages within a set time period can be used as the relevance information between the set of group messages associated with the target online document within the set time period and the candidate online document.

[0178] Terminal 213 can obtain the popularity of the target online document by weighting it based on relevance and one or more combinations of the methods mentioned above, or by inputting it into a popularity prediction model based on relevance and one or more combinations of the methods mentioned above, or by obtaining the popularity of the target online document. The specific methods for weighting or obtaining the popularity prediction model can be referred to the previous discussion, and will not be repeated here.

[0179] For example, please refer to Figure 8 For group messages displayed in the chat window within a set time period, the online document whose relevance to the group message set needs to be determined is named "Meeting Minutes". After obtaining these group messages, the first server 221 identifies that the number of group messages mentioning "Meeting Minutes" within the set time period is 4, while the total number of group messages within the set time period is 5. Therefore, the relevance of this target online document to the group message set can be represented as 4 / 5. If the name of another target online document is "Financial Statements", and there are 0 group messages mentioning "Financial Statements" in the group message set, then the relevance of this target online document to the group message set is 0.

[0180] S509, the third server 223 obtains at least one target online document based on popularity.

[0181] After obtaining the popularity of each candidate online document, the third server 223 can sort the candidate online documents according to their popularity from high to low, obtain a sorted list of candidate online documents, and select at least one target online document from the top of the sorted list. Alternatively, the third server 223 can directly determine the candidate online documents whose popularity is greater than or equal to a popularity threshold from the multiple candidate online documents, and select the candidate online documents whose popularity is greater than or equal to the popularity threshold as the target online documents.

[0182] S510, the third server 223 sends the display information of at least one target online document to the first server 221.

[0183] After the third server 223 determines the target online document from multiple candidate online documents, it can obtain the display information of the target online document from the database 230 or from the previously obtained usage records, and send the display information of the target online document to the first server 221.

[0184] S511, the first server 221 obtains the target tag information of each target online document.

[0185] The first server 221 extracts key information from the usage records of the target online document and generates alternative tag information for the target online document based on the key information.

[0186] This involves how the first server 221 generates candidate tag information based on key information. The first server 221 can extract key information of the target online document from one or both of the time attribute information or collaboration information of the target online document in the usage record. The first server 221 can directly use the key information as candidate tag information, or the first server 221 can combine the key information from at least one key information to obtain candidate tag information, or the first server 221 can use both the key information and the combination of key information as candidate tag information.

[0187] After obtaining the candidate tag information, the first server 221 can filter out the candidate tag information from multiple candidate tag information that meets the relevance threshold with the target online document, and determine the candidate tag information that meets the relevance threshold with the target online document as the target tag information of the target online document. The specific method by which the first server 221 filters the candidate tag information that meets the relevance threshold is illustrated below with an example:

[0188] As discussed earlier, each target online document includes corresponding keywords. We can determine the candidate tag information for the target online document and its keywords. The candidate tag information that matches the keywords is then used as the candidate tag information for the target online document. Matching is defined as either the candidate tag information being the same as the keyword, or the similarity between the encoding vector of the candidate tag information and the encoding vector of the keyword being greater than or equal to a similarity threshold.

[0189] Alternatively, the first server 221 determines the name of the target online document and its similarity to each candidate tag information, and determines the candidate tag information with a similarity greater than or equal to the similarity threshold as the target tag information.

[0190] As one embodiment, the first server 221 may generate or update the target tag information of the online document in real time each time it stores the online document or when the online document is used. After the first server 221 obtains the display information of the target online document, it obtains multiple candidate tag information from the database 230, and then obtains the target tag information of the target online document from the multiple candidate tag information.

[0191] S512, the first server 221 sends the display information of at least one target online document and the target tag information to the acquisition terminal 213.

[0192] After receiving display information of at least one target online document and target tag information from the third server 223, the first server 221 associates the display information of the target online document with the target tag information, and forwards the target tag information and the associated display information of the target online document to the acquisition terminal 213, which is equivalent to the acquisition terminal 213 obtaining the acquisition result corresponding to the search request.

[0193] S513, obtain display information of at least one target online document displayed by terminal 213, as well as target tag information.

[0194] After obtaining the display information of each target online document in at least one target online document, as well as the associated target tag information, the terminal 213 can display the target online document in association with its associated target tag information.

[0195] In this embodiment, the display information of the target online document can be associated with the target tag information. Even when the results include multiple target online documents with similar names, the target user can further distinguish the target online documents based on the target tag information, without the user having to open each target online document one by one, allowing the user to quickly identify the target online document they want.

[0196] As one embodiment, when displaying target online documents, the acquisition terminal 213 can display each target online document in descending order of popularity.

[0197] Since the target online document with high popularity in this application embodiment is more likely to be clicked by the target user, it is more conducive to improving the click-through rate (CTR) of the first result of the online document and the NDCG of the presented target online document.

[0198] In another embodiment, the acquisition terminal 213 may also display display information of at least one target online document without displaying target tag information.

[0199] Obtain terminal 213 via Figure 6 Taking the search request generated by the corresponding page as an example, after obtaining at least one target online document from terminal 213, it can display as follows: Figure 9 The interface shown is a display of the target online documents. This interface displays the name 901 of each target online document, the last viewed time 903, and the creator 904. The name 901 of the target online document is as follows: Figure 9 The "Meeting Minutes" shown here were last viewed 903 times. Figure 9 The dates shown as "March 1st, March 2nd, March 20th, and April 20th, etc." are specifically created by 904. Figure 9 The labels "A, B, C, and D" are shown. In addition, the display interface includes target tag information 902 for each online document. For example, the target tag information for the online document displayed in the first row specifically includes tags such as "Viewed by 6 people in the product design team this week, newly created, currently viewed by 3 people, edited by 7 people today," etc.

[0200] Alternatively, to obtain terminal 213 via Figure 7 Taking the search operation performed on the group-related search interface shown as an example, after terminal 213 obtains at least one target online document, it can display as follows: Figure 10 The search feedback interface for the target online document shown displays the name (1001), the most recent viewing time (1002), and the creator (1003) of each target online document. For example, the name (1001) of the target online document... Figure 10 The "Meeting Minutes 1" shown here is an online document whose most recent view time is 1002. Figure 10 The "March 1st" shown refers to the creator of the target online document, for example, 1003. Figure 10The search results also show the target online document's tag information (1004), specifically as follows: Figure 10 The “Product Design Group 6 Weekly Browse” shown here.

[0201] In some cases, the processing server 220 may still select a large number of target online documents for the target user, or the selected target online documents may not fully meet the user's needs. While viewing at least one target online document, the user may complain to other users or talk to themselves. In this situation, the acquisition terminal 213 can collect the target user's speech information, which may specifically be voice information or dialogue information. The acquisition terminal 213 then sends the target user's speech information to the processing server 220. This involves how the acquisition terminal 213 determines whether the target user is currently viewing at least one target online document. For example, the acquisition terminal 213 can determine whether the target user is performing other operations. If the target user is not performing other operations, it is determined that the target user is viewing at least one target online document. Other operations refer to actions other than those related to viewing at least one target online document.

[0202] After receiving the discourse information, the processing server 220 can parse the sorting criteria in the discourse information, convert the discourse information into text content, extract keywords related to the sorting criteria from the text content, thereby obtaining the sorting criteria corresponding to the keywords, and then filter out the reselected online documents from the target online documents according to the sorting criteria, and send the display information of the reselected online documents back to the acquisition terminal 213. After receiving the display information of the reselected online documents, the acquisition terminal 213 can update the display information of at least one target online document to the display information of the reselected online documents.

[0203] In this embodiment, it is equivalent to further filtering at least one target online document to obtain results that better meet the needs of the target user.

[0204] For example, when a target user views at least one online document, they might mention, "I remember A posted this a couple of days ago... but I can't find it." In this case, the acquisition terminal 213 can obtain this utterance information and send it to the processing server 220. After receiving the utterance information, the processing server 220 parses out the keywords "A" and "a couple of days ago" in the utterance information. Then, the processing server 220 can filter out online documents related to "A" and "a couple of days ago" from the target online documents and feed these reselected online documents back to the acquisition terminal 213.

[0205] It should be noted that, Figure 5The example described uses server 220, which includes a first server 221, a second server 222, and a third server 223. However, server 220 can also be implemented using a single server. Figure 5 The interaction steps between the various servers shown are optional. Based on the above... Figure 4 The application scenarios discussed below will be used to describe the online document processing methods involved in the embodiments of this application.

[0206] Please refer to Figure 11 This represents a schematic diagram of the interaction between the devices, and the specific interaction process includes:

[0207] S1101, terminal 213 generates a recommendation request based on the target user's target operation.

[0208] When a user needs to obtain recommendations for online documents in certain situations, such as when a user wants to insert an online document while editing, the user can perform the target operation of inserting an online document. Based on this target operation, the acquisition terminal 213 generates a recommendation request. This recommendation request recommends relevant online documents to the target user. The recommendation request may carry the name of the online document the target user is editing, and may also include the terminal identifier of the acquisition terminal 213 corresponding to the target user. The recommendation request may also include recommendation keywords, which indicate that the target user wants to find online documents related to those recommendation keywords.

[0209] When users are Figure 4 In the online document editing interface, an insertion operation is performed, and terminal 213 generates a recommendation request based on the insertion operation.

[0210] S1102, terminal 213 sends a recommendation request to processing server 220.

[0211] S1103, Processing server 220 retrieves multiple alternative online documents associated with the recommendation request.

[0212] If the recommendation request includes a recommendation keyword, the processing server 220 can retrieve multiple alternative online documents associated with that keyword. If the recommendation request does not include a recommendation keyword, then the multiple alternative online documents can be all online documents associated with the user.

[0213] S1104, Process server 220 query usage records.

[0214] For information on how to query usage records and related content, please refer to the previous discussion; it will not be repeated here.

[0215] S1105, Processing server 220 retrieves usage records from database 230.

[0216] As one embodiment, steps S1104 to S1105 are optional.

[0217] S1106, Processing server 220 obtains the popularity of each candidate online document based on the usage records of each candidate online document.

[0218] The methods for gaining popularity can be referred to the content discussed above, and will not be repeated here.

[0219] S1107, Processing server 220 obtains at least one target online document based on popularity.

[0220] The method of obtaining target online documents based on popularity can be referred to the previous discussion, and will not be repeated here.

[0221] S1108, Processing server 220 obtains target tag information for each target online document.

[0222] The target tag information and the methods for obtaining target tag information can be referred to the previous discussion, and will not be repeated here.

[0223] As one embodiment, S1108 is an optional step.

[0224] S1109, the processing server 220 sends the display information of at least one target online document and the target tag information to the acquisition terminal 213.

[0225] S1110, Obtain display information of at least one target online document displayed by terminal 213, as well as target tag information.

[0226] The methods for obtaining the display information and target label information of terminal 213 can refer to the content discussed above, and will not be repeated here.

[0227] In another embodiment, if the processing server 220 does not acquire target tag information, then in S1109 the processing server 220 only needs to send display information of at least one target online document, and in S1110 the acquiring terminal 213 also only displays display information of at least one target online document.

[0228] For example, please refer to Figure 12 After the user performs an insertion operation, the terminal 213 receives the response to the insertion operation, generates a recommendation request, and displays the recommendation results based on the feedback from the processing server 220. Figure 12 The interface shown includes information on the recommended online documents and their target tags.

[0229] In one possible embodiment, the processing server 220 can receive utterance information from a target user when viewing the display information of at least one target online document; parse the sorting criteria in the utterance information to obtain at least one reselected online document that satisfies the sorting criteria, and display the display information of the at least one reselected online document. The specific content of the reselected online document can be referred to the preceding discussion, and will not be repeated here.

[0230] Based on the same inventive concept, embodiments of this application provide an online document processing apparatus, which is equivalent to the processing server 220 described above. Please refer to... Figure 13 The online document processing device 1300 includes:

[0231] The transceiver module 1301 is used to receive requests from target users for online documents;

[0232] The determining module 1302 is used to determine multiple candidate online documents from the online documents within the target user's permissions according to the acquisition request;

[0233] The module 1303 is used to obtain the popularity of each candidate online document based on the usage records of each candidate online document; wherein the popularity is used to characterize one or a combination of the usage trend and the timeliness of the candidate online document; and to obtain at least one target online document whose popularity meets the set conditions from the plurality of candidate online documents.

[0234] The transceiver module 1301 is also used to respond to the acquisition request and send the display information of the at least one target online document.

[0235] In one possible embodiment, the transceiver module 1301 is further configured to:

[0236] When responding to a request, the system sends display information for at least one target online document, as well as target tag information for each target online document. The target tag information includes the time attribute and collaboration information of the target online document.

[0237] In one possible embodiment, the target tag information of the target online document is obtained in the following way:

[0238] Based on the usage records of each target online document, extract key information from one or two of the time attributes and collaboration information of each target online document;

[0239] Based on the key information of each target document, obtain multiple alternative tag information for each target online document;

[0240] For each target online document, the candidate tag information whose relevance meets the relevance threshold among multiple candidate tag information of the target online document is determined as the target tag information of the target online document.

[0241] In one possible embodiment, the obtaining module 1303 is specifically used for:

[0242] Get one or any combination of the following information within a set time period: the frequency of access to online documents, the duration of each access, and the time interval between the access time to the candidate online document and the current time.

[0243] Based on the information obtained, the popularity of each candidate online document was determined. The higher the access frequency, the higher the popularity; the longer the access time, the higher the popularity; and the shorter the time interval, the higher the popularity.

[0244] In one possible embodiment, the obtaining module 1303 is further specifically used for:

[0245] Obtain the relevance information between the set of group messages associated with the target group within a set time period and the candidate online documents, and

[0246] When determining the popularity of each candidate online document based on the information obtained, the greater the relevance, the higher the popularity.

[0247] In one possible embodiment, the obtaining module 1303 is specifically used to obtain the popularity of candidate online documents in any of the following ways:

[0248] Based on access frequency information, the access frequency corresponding to each type of access operation for the candidate online documents is obtained. Then, a weighted sum of the access frequencies for each type of access operation for the candidate online documents is calculated to obtain the popularity of the candidate online documents. The weighting weight for each type of access operation is different; or...

[0249] Based on the duration of each access, the access duration of each user to the candidate online documents is obtained, and the access durations of all users related to the target user are weighted and summed, with users having a greater relevance to the target user receiving a larger weight; or,

[0250] Input the time interval between the access time to the candidate online document and the current time into an inverse correlation function to obtain the popularity of the candidate online document.

[0251] In one possible embodiment, the transceiver module 1301 is further configured to receive speech information of the target user when viewing the display information of at least one target online document after sending the display information of at least one target online document in response to the acquisition request.

[0252] The module 1303 is also used to parse the sorting criteria in the discourse information, obtain at least one target online document that meets at least one reselected online document, and display the information of at least one reselected online document.

[0253] Based on the same inventive concept, embodiments of this application provide an online document processing device, which is equivalent to the acquisition terminal 213 described above. Please refer to... Figure 14 The online document processing device 1400 includes:

[0254] The sending module 1401 is used to send a recommendation request in response to the target user's target operation of inserting an online document;

[0255] The receiving module 1402 is used to receive display information of at least one target online document and target tag information of each target online document; wherein, the target tag information includes the time attribute and collaboration information of the target online document;

[0256] Display module 1403 is used to display display information of at least one target online document, as well as target tag information for each target online document.

[0257] In one possible embodiment, the sending module 1401 is specifically used for:

[0258] In response to a target user's targeted action of searching for online documents via a webpage, a search request is sent; or,

[0259] In response to a target user's action of searching for online documents in the chat window of an instant messaging client, a search request is sent.

[0260] In one possible embodiment, the display module 1403 is specifically used for:

[0261] Display information for each target online document in at least one target online document, arranged in descending order of popularity.

[0262] Based on the same inventive concept, embodiments of this application provide an online document processing device, which is equivalent to the acquisition terminal 213 described above. Please refer to... Figure 15 The online document processing device 1500 includes:

[0263] The sending module 1501 is used to send a recommendation request in response to the target user's target operation of inserting an online document;

[0264] The receiving module 1502 is used to display the display information of at least one target online document, as well as the target tag information of each target online document; wherein, the target tag information includes the time attribute and collaboration information of the target online document;

[0265] Display module 1503 is used to display the display information of at least one target online document, as well as the target tag information of each target online document; wherein, the target tag information includes the time attribute and collaboration information of the target online document.

[0266] In one possible embodiment, the display module 1503 is specifically used for:

[0267] Display information for each target online document in at least one target online document, arranged in descending order of popularity.

[0268] Based on the same inventive concept, this application also provides a computer device. This computer device is equivalent to the processing server 220 discussed above.

[0269] Please refer to Figure 16 The computer device 1600 is manifested in the form of a general-purpose computer device. The components of the computer device 1600 may include, but are not limited to: at least one processor 1610, at least one memory 1620, and a bus 1630 connecting different system components (including the processor 1610 and the memory 1620).

[0270] Bus 1630 represents one or more of several bus architectures, including a memory bus or memory controller, peripheral bus, processor, or a local bus using any of the various bus architectures.

[0271] Memory 1620 may include readable media in the form of volatile memory, such as random access memory (RAM) 1621 and / or cache memory 1622, and may further include read-only memory (ROM) 1623. Memory 1620 may also include a program / utility 1626 having a set (at least one) of program modules 1625, such program modules 1625 including, but not limited to, an operating system, one or more application programs, other program modules, and program data; each or some combination of these examples may include an implementation of a network environment. Processor 1610 is used to execute program instructions stored in memory 1620 to implement the online document processing method discussed above. Processor 1610, by executing program instructions stored in memory 1620, can also implement the functions of the processing server 220 discussed above, and... Figures 13-15 The function of an online document processing device for any discussion.

[0272] Computer device 1600 can also communicate with one or more external devices 1640 (e.g., keyboard, pointing device, etc.), one or more devices that enable terminal devices to interact with computer device 1600, and / or any device that enables computer device 1600 to communicate with one or more other devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 1650. Furthermore, computer device 1600 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 1660. As shown, network adapter 1660 communicates with other modules used with computer device 1600 via bus 1630. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with computer device 1600, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0273] Based on the same inventive concept, embodiments of this application provide a storage medium storing computer instructions that, when executed on a computer, cause the computer to perform the online document processing method described above. In this application, "storage medium" refers generally to a computer-readable storage medium.

[0274] Based on the same inventive concept, embodiments of this application provide a computer program product including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform any of the online document processing methods described above.

[0275] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0276] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for processing online documents, characterized in that, include: Receive requests from target users for online documents; Based on the acquisition request, multiple candidate online documents are determined from the online documents within the target user's permissions; Based on the usage records of each candidate online document, the popularity of each candidate online document is obtained, including: determining the first popularity based on the access frequency of the online document within a set time period; determining the second popularity based on the duration of each access to the online document within the set time period; and determining the third popularity based on the time interval between the access time of the candidate online document and the current time. The higher the access frequency, the higher the popularity; the longer the access duration, the higher the popularity; and the shorter the time interval, the higher the popularity. When the target user sends a confirmation request through the application interface associated with the target group, the relationship between the group message set associated with the target group within the set time period and the candidate online documents is obtained. The relevance information is used to determine a fourth popularity level, with higher relevance indicating higher popularity. The popularity of the candidate online document is determined based on at least two of the first, second, third, and fourth popularity levels. The popularity level characterizes one or a combination of the usage trend and timeliness of the candidate online document. The usage record refers to the usage records generated by users accessing online documents within the same shared space, including the time attribute information and collaboration information of the online document. The usage record includes the target user's own usage record and the usage records of other users within the same shared space. From the plurality of candidate online documents, obtain at least one target online document whose popularity meets the set conditions; In response to the acquisition request, display information of the at least one target online document is sent.

2. The method as described in claim 1, characterized in that, The step of determining the first popularity based on the access frequency information of online documents within a set time period includes: Based on the usage records of the candidate online documents, the access operation types of multiple users accessing the candidate online documents within a set time period are obtained; the access frequency under each access operation type is counted; the access frequencies under different access operation types are weighted, and the weighted results under all access operation types are summed to determine the first popularity; wherein, the longer the operation time of the access operation type, the greater the weighting weight, and the operation time of any access operation type is the average operation time of the multiple users when performing the corresponding type of access operation.

3. The method as described in claim 1, characterized in that, The step of determining the second popularity based on the duration of each access to an online document within a set time period includes: Based on the usage records of the candidate online documents, the duration of each user's access to the candidate online documents is determined, and the durations of access for different users are weighted to obtain a second popularity index. Among them, the user with a greater relevance to the target user has a greater weight for the duration of access, and the relevance is determined based on the frequency of message interaction with the target user.

4. The method as described in claim 1, characterized in that, The process of determining the third popularity level based on the time interval between the time of accessing the candidate online documents and the current time includes: The third popularity level is determined based on the time interval between the last access time of the candidate online document and the current time.

5. The method as described in claim 1, characterized in that, The step of obtaining the relevance information between the set of group messages associated with the target group within a set time period and the candidate online documents, and determining the fourth popularity based on the relevance information, includes: Based on the ratio between the number of group messages related to the candidate online documents within the target group and the total number of group messages within a set time period, relevance information is determined; based on the relevance information, a fourth popularity index is determined, wherein the relevant information includes at least one of name and keyword.

6. The method as described in claim 1, characterized in that, The method further includes: In response to the acquisition request, when sending the display information of the at least one target online document, target tag information of each target online document is also sent, wherein the target tag information includes the time attribute information and collaboration information of the corresponding target online document, and the display information of the target online document and the target tag information are used for associated display.

7. The method as described in claim 6, characterized in that, The target tag information for the target online document is obtained in the following ways: Based on the usage records of each target online document, extract key information of the target online document from one or two of the time attributes and collaboration information of each target online document; Based on the key information of each target document, obtain multiple alternative tag information for each target online document; For each target online document, the candidate tag information whose relevance meets the relevance threshold among the multiple candidate tag information of the target online document is determined as the target tag information of the target online document.

8. The method as described in claim 1, characterized in that, The method further includes: Each type of access operation has a different weighting; or, Based on the duration of each access, the access duration of each user to the candidate online documents is obtained, and the access durations of all users related to the target user are weighted and summed, where users with greater relevance to the target user have larger weights; or, The time interval between the access time to the candidate online document and the current time is input into an inverse correlation function to obtain the third popularity.

9. The method according to any one of claims 1 to 6, characterized in that, After responding to the acquisition request and sending the display information of the at least one target online document, the process includes: Receive the speech information of the target user when viewing the display information of the at least one target online document; The sorting criteria in the discourse information are parsed to obtain at least one reselected online document that satisfies the sorting criteria, and the display information of the at least one reselected online document is sent.

10. The method according to any one of claims 1 to 6, characterized in that, The same shared space includes members of the same company, members of the same group, or members under the same organizational structure. Receiving a target user's request to obtain an online document includes at least receiving a target user's request to obtain an online document in the target group.

11. A method for processing online documents, characterized in that, include: In response to a target user's action of searching for online documents, a search request is sent to the server; The system receives and displays display information of at least one target online document sent by the server, as well as target tag information for each target online document; wherein the target tag information includes time attribute information and collaboration information corresponding to the target online document, and the collaboration information includes the collaboration information of the target user on the online document, as well as the collaboration information of other users in the same shared space as the target user on the online document; The process by which the server sends the display information and target tag information of the target online document includes: Based on the search request, multiple candidate online documents are determined from the online documents within the target user's permissions; Based on the usage records of each candidate online document, the popularity of each candidate online document is obtained, including: determining the first popularity based on the access frequency information of the online document within a set time period; determining the second popularity based on the duration of each access to the online document within the set time period; determining the third popularity based on the time interval between the access time of the candidate online document and the current time; wherein, the higher the access frequency, the higher the popularity; the longer the access duration, the higher the popularity; and the shorter the time interval, the higher the popularity. When the target user sends a confirmation request through the application interface associated with the target group, the relevance information between the group message set associated with the target group within the set time period and the candidate online document is obtained. Based on the relevance information, a fourth popularity is determined, with the higher the relevance, the higher the popularity. The popularity of the candidate online documents is determined from at least two of the first popularity, the second popularity, the third popularity, and the fourth popularity; wherein, the popularity is used to characterize one or a combination of the usage trend and the timeliness of the candidate online documents, the usage record refers to the usage record generated by each user accessing the online document in the same shared space, including the time attribute information and collaboration information of the online document, and the usage record includes the usage record of the target user and the usage records of other users in the same shared space as the target user; at least one target online document whose popularity meets the set conditions is obtained from the plurality of candidate online documents, and in response to the search request, the display information and target tag information of the at least one target online document are sent.

12. The method as described in claim 11, characterized in that, The action of a target user searching for online documents involves sending a search request to the server, including any of the following: In response to a target user's action of searching for online documents via a webpage, a search request is sent to the server; or... The system responds to the target user's search for online documents in the chat window of the instant messaging client by sending a search request to the server.

13. A method for processing online documents, characterized in that, include: In response to the target user's action of inserting an online document, a recommendation request is sent to the server; The system receives and displays display information of at least one target online document sent by the server, as well as target tag information for each target online document; wherein the target tag information includes time attribute information and collaboration information corresponding to the target online document, and the collaboration information includes the collaboration information of the target user on the online document, as well as the collaboration information of other users in the same shared space as the target user on the online document; The process by which the server sends the display information and target tag information of the target online document includes: Based on the recommendation request, multiple alternative online documents are determined from the online documents within the target user's permissions; Based on the usage records of each candidate online document, the popularity of each candidate online document is obtained, including: determining the first popularity based on the access frequency information of the online document within a set time period; determining the second popularity based on the duration of each access to the online document within the set time period; determining the third popularity based on the time interval between the access time of the candidate online document and the current time; wherein, the higher the access frequency, the higher the popularity; the longer the access duration, the higher the popularity; and the shorter the time interval, the higher the popularity. When the target user sends a confirmation request through the application interface associated with the target group, the relevance information between the group message set associated with the target group within the set time period and the candidate online document is obtained. Based on the relevance information, a fourth popularity is determined, with the higher the relevance, the higher the popularity. The popularity of the candidate online documents is determined from at least two of the first popularity, the second popularity, the third popularity, and the fourth popularity; wherein, the popularity is used to characterize one or a combination of the usage trend and the timeliness of the candidate online documents, the usage record refers to the usage record generated by each user accessing the online document in the same shared space, including the time attribute information and collaboration information of the online document, and the usage record includes the usage record of the target user and the usage records of other users in the same shared space as the target user; at least one target online document whose popularity meets the set conditions is obtained from the plurality of candidate online documents, and in response to the recommendation request, the display information and target tag information of the at least one target online document are sent.

14. An online document processing apparatus, characterized in that, include: The send / receive module is used to receive requests from target users for online documents; The determination module is used to determine multiple candidate online documents from the online documents within the target user's permissions based on the acquisition request; The acquisition module is used to obtain the popularity of each candidate online document based on its usage records. This includes: determining a first popularity based on the frequency of access to the online document within a set time period; determining a second popularity based on the duration of each access within the set time period; and determining a third popularity based on the time interval between the access time of the candidate online document and the current time. Higher access frequency, longer access duration, and shorter time intervals all contribute to higher popularity. When the target user sends a confirmation request through the application interface associated with the target group, the module obtains the relevance information between the group message set associated with the target group within the set time period and the candidate online documents. Based on this relevance information... The system determines a fourth popularity metric, with higher relevance indicating higher popularity. It then determines the popularity of candidate online documents based on at least two of the first, second, third, and fourth popularity metrics. The popularity metric characterizes one or a combination of the usage trend and timeliness of the candidate online documents. Finally, it obtains at least one target online document from the plurality of candidate online documents whose popularity meets set conditions. The usage record refers to the usage records generated by users accessing online documents within the same shared space, including the time attribute information and collaboration information of the online document. The usage record includes the target user's own usage record and the usage records of other users within the same shared space as the target user. The transceiver module is also used to respond to the acquisition request and send the display information of the at least one target online document.

15. An online document processing apparatus, characterized in that, include: The sending module is used to send a search request to the server in response to the target user's target action of searching for online documents; A receiving module is configured to receive display information of at least one target online document and target tag information for each target online document sent by the server. The target tag information includes time attribute information and collaboration information corresponding to the target online document. The collaboration information includes collaboration information of the target user on the online document and collaboration information of other users in the same shared space as the target user on the online document. The process of the server sending the display information and target tag information of the target online document includes: determining multiple candidate online documents from the online documents within the target user's permissions according to the search request; obtaining the popularity of each candidate online document based on its usage records, including: determining a first popularity based on the access frequency information of the online document within a set time period; determining a second popularity based on the duration information of each access to the online document within the set time period; and determining a third popularity based on the time interval information between the access time of the candidate online document and the current time. The higher the access frequency and the longer the access duration, the higher the popularity. Furthermore, the shorter the time interval, the higher the popularity. When the target user sends a confirmation request through the application interface associated with the target group, the relevance information between the group message set associated with the target group within a set time period and the candidate online documents is obtained. Based on the relevance information, a fourth popularity is determined, with higher relevance indicating higher popularity. The popularity of the candidate online documents is determined based on at least two of the first popularity, second popularity, third popularity, and fourth popularity. The popularity is used to characterize one or a combination of the usage trend and timeliness of the candidate online documents. The usage record refers to the usage record generated by each user accessing online documents in the same shared space, including the time attribute information and collaboration information of the online documents. The usage record includes the target user's own usage record and the usage records of other users in the same shared space as the target user. At least one target online document whose popularity meets the set conditions is obtained from the multiple candidate online documents. In response to the search request, the display information and target tag information of the at least one target online document are sent. The display module is used to display the presentation information of the at least one target online document, as well as the target tag information of each target online document.

16. An online document processing apparatus, characterized in that, The sending module is used to send a recommendation request to the server in response to the target user's target operation of inserting an online document; A receiving module is configured to receive display information of at least one target online document and target tag information for each target online document sent by the server. The target tag information includes time attribute information and collaboration information corresponding to the target online document. The collaboration information includes collaboration information of the target user on the online document and collaboration information of other users in the same shared space as the target user on the online document. The process of the server sending the display information and target tag information of the target online document includes: determining multiple candidate online documents from the online documents within the target user's permissions according to the recommendation request; obtaining the popularity of each candidate online document based on its usage records, including: determining a first popularity based on the access frequency information of the online document within a set time period; determining a second popularity based on the duration information of each access to the online document within the set time period; and determining a third popularity based on the time interval information between the access time of the candidate online document and the current time. The higher the access frequency and the longer the access duration, the higher the popularity. Furthermore, the shorter the time interval, the higher the popularity. When the target user sends a confirmation request through the application interface associated with the target group, the relevance information between the group message set associated with the target group within a set time period and the candidate online documents is obtained. Based on the relevance information, a fourth popularity is determined, with higher relevance indicating higher popularity. The popularity of the candidate online documents is determined based on at least two of the first popularity, second popularity, third popularity, and fourth popularity. The popularity is used to characterize one or a combination of the usage trend and timeliness of the candidate online documents. The usage record refers to the usage record generated by each user accessing online documents in the same shared space, including the time attribute information and collaboration information of the online documents. The usage record includes the target user's own usage record and the usage records of other users in the same shared space as the target user. At least one target online document whose popularity meets the set conditions is obtained from the multiple candidate online documents. In response to the recommendation request, the display information and target tag information of the at least one target online document are sent. The display module is used to display the presentation information of at least one target online document, as well as the target tag information of each target online document; wherein, the target tag information includes the time attribute and collaboration information of the target online document.

17. A computer device, characterized in that, include: At least one processor, and A memory that is communicatively connected to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the at least one processor implements the method as described in any one of claims 1-10, 11-12, or 13 by executing the instructions stored in the memory.

18. A storage medium, characterized in that, The storage medium stores computer instructions that, when executed on a computer, cause the computer to perform the method as described in any one of claims 1-10, 11-12, or 13.

19. A computer program product comprising computer instructions, characterized in that, When the computer instructions are executed by the processor, they implement the steps of the method according to any one of claims 1-10, 11-12, or 13.

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