Information recommendation method, apparatus, device, and medium

Abstract

This disclosure relates to an information recommendation method, apparatus, device, and medium. The information recommendation method includes: acquiring raw user data; extracting data from the raw data according to preset data extraction conditions to obtain at least one first data point, wherein the at least one first data point is associated with the preset data extraction conditions; classifying the at least one first data point according to a preset theme to obtain second data point corresponding to the preset theme, wherein the second data point is one or more of the at least one first data point; performing data analysis on the second data to obtain user needs; and recommending information based on user needs. According to embodiments of this disclosure, manufacturers can recommend useful information to users based on their actual needs, achieving accurate information recommendation and improving user experience.

Description

Technical Field

[0001] This disclosure relates to the field of information processing technology, and in particular to an information recommendation method, apparatus, device, and medium. Background Technology

[0002] Currently, existing information recommendation methods are mainly based on real-time monitoring of users' browsing information and voice information from electronic products such as mobile phones or computers. For example, if a user repeatedly says "buy clothes" into their phone, relevant clothing information can be recommended to the user. However, there is now a need to analyze users' browsing data to determine their specific needs and then make information recommendations accordingly. Summary of the Invention

[0003] To address the aforementioned technical problems, this disclosure provides an information recommendation method, apparatus, device, and medium.

[0004] A first aspect of this disclosure provides an information recommendation method, including:

[0005] Obtain the user's raw data;

[0006] According to preset data extraction conditions, data is extracted from the original data to obtain at least one first data, wherein at least one first data is associated with the preset data extraction conditions;

[0007] At least one first data point is classified according to a preset theme to obtain second data corresponding to the preset theme, wherein the second data point is one or more of the at least one first data point;

[0008] Data analysis was performed on the second set of data to determine user needs;

[0009] Information recommendations are made based on user needs.

[0010] A second aspect of this disclosure provides an information recommendation device, comprising:

[0011] The data acquisition module is used to acquire the user's raw data;

[0012] The data extraction module is used to extract data from the original data according to preset data extraction conditions to obtain at least one first data, wherein the at least one first data is associated with the preset data extraction conditions.

[0013] The data classification module is used to classify at least one first data according to a preset theme to obtain second data corresponding to the preset theme, wherein the second data is one or more of the at least one first data;

[0014] The data analysis module is used to analyze the second set of data to obtain user requirements;

[0015] The information recommendation module is used to recommend information based on user needs.

[0016] A third aspect of this disclosure provides an electronic device, including:

[0017] processor;

[0018] Memory, used to store executable instructions;

[0019] The processor is used to read executable instructions from memory and execute the executable instructions to implement the information recommendation method provided in the first aspect above.

[0020] A fourth aspect of this disclosure provides a computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to implement the information recommendation method provided in the first aspect.

[0021] The technical solution provided in this disclosure has the following advantages compared with the prior art:

[0022] The information recommendation method, apparatus, device, and medium provided in this disclosure can obtain user's raw data, extract data from the raw data according to preset data extraction conditions, and obtain at least one first data, wherein the at least one first data is associated with the preset data extraction conditions. After obtaining the at least one first data, the at least one first data is classified according to a preset theme to obtain second data corresponding to the preset theme, wherein the second data is one or more of the at least one first data. Data analysis is performed on the second data to obtain user needs, and then information recommendation is performed based on user needs. Thus, manufacturers can recommend useful information to users according to the users' actual needs, achieving accurate information recommendation and improving user experience. Attached Figure Description

[0023] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0024] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a flowchart of an information recommendation method provided in an embodiment of this disclosure;

[0026] Figure 2 This is a flowchart of another information recommendation method provided in this embodiment of the disclosure;

[0027] Figure 3 This is a schematic diagram of the structure of an information recommendation device provided in an embodiment of this disclosure;

[0028] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this disclosure. Detailed Implementation

[0029] To better understand the above-mentioned objectives, features, and advantages of this disclosure, the solutions disclosed herein will be further described below. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0030] Numerous specific details are set forth in the following description in order to provide a full understanding of this disclosure, but this disclosure may also be implemented in other ways different from those described herein; obviously, the embodiments in the specification are only some, and not all, of the embodiments of this disclosure.

[0031] It should be understood that the steps described in the method embodiments of this disclosure may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of this disclosure is not limited in this respect.

[0032] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0033] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0034] Existing information recommendation methods typically fail to provide accurate information recommendations based on users' actual needs, thus hindering customization and preventing manufacturers from recommending useful information. Consequently, the value of the recommended information is not fully realized, resulting in a diminished user experience. To address this issue, this disclosure provides an information recommendation method, which will be described below with reference to specific embodiments.

[0035] Figure 1 This is a flowchart of an information recommendation method provided in an embodiment of the present disclosure. The method can be executed by an information recommendation device, which can be implemented in software and / or hardware. The information recommendation device can be configured in an electronic device, such as a server or terminal, wherein the terminal specifically includes a mobile phone, computer or tablet computer, etc.

[0036] like Figure 1 As shown, the information recommendation method provided in this embodiment includes the following steps.

[0037] S110, Obtain the user's raw data.

[0038] In this embodiment of the disclosure, the electronic device can obtain the user's raw data when it is necessary to make information recommendations to the user.

[0039] Alternatively, raw data can be understood as data corresponding to all of the user's browsing history, such as the user's video browsing data, the user's shopping browsing data, the user's web browsing data, etc.

[0040] In some embodiments of this disclosure, the electronic device can retrieve the user's raw data from a pre-stored database.

[0041] In other embodiments of this disclosure, the electronic device can acquire the user's raw data in real time through a preset data reading tool.

[0042] S120. According to the preset data extraction conditions, the original data is extracted to obtain at least one first data, wherein the at least one first data is associated with the preset data extraction conditions.

[0043] In this embodiment of the disclosure, after the electronic device obtains the user's original data, it extracts data from the original data according to preset data extraction conditions to obtain at least one first data, wherein the at least one first data is associated with the preset data extraction conditions.

[0044] Among them, the preset data extraction conditions are the pre-set conditions for data extraction, and different preset data extraction conditions can be set according to different manufacturers.

[0045] For example, when the manufacturer is a clothing manufacturer, the preset data extraction conditions can be clothing-related extraction conditions. Based on the preset data extraction conditions, only clothing-related data can be extracted from the original data. When the manufacturer is an entertainment video manufacturer, the preset data extraction conditions can be entertainment video-related extraction conditions. Based on the preset data extraction conditions, only entertainment video-related data can be extracted from the original data.

[0046] Optionally, the electronic device can extract at least one first data point from the original data that corresponds one-to-one with each of the multiple different preset data extraction conditions, based on multiple different preset data extraction conditions.

[0047] S130. Classify at least one first data according to a preset theme to obtain second data corresponding to the preset theme, wherein the second data is one or more of at least one first data.

[0048] In this embodiment of the disclosure, after acquiring at least one first data, the electronic device classifies the at least one first data according to a preset topic to obtain second data corresponding to the preset topic, wherein the second data is one or more of the at least one first data.

[0049] In this embodiment of the disclosure, the preset topic is the topic corresponding to the preset data extraction conditions.

[0050] For example, if the preset data extraction condition is related to clothing, then the preset topic is also related to the preset data extraction condition. For example, the preset topic could be tops, pants, socks, etc.

[0051] Optionally, the second data corresponding to the preset theme can be one or more of at least one of the first data.

[0052] Specifically, after acquiring at least one first data, the electronic device classifies the at least one first data according to a preset theme corresponding to preset data extraction conditions, and then obtains the second data corresponding to the preset theme.

[0053] S140. Perform data analysis on the second data to obtain user requirements.

[0054] In this embodiment of the disclosure, after acquiring the second data, the electronic device performs data analysis on the second data to obtain user requirements.

[0055] Alternatively, user needs can be understood as different user-related needs obtained by different manufacturers based on the user's second data.

[0056] For example, when the manufacturer is a clothing manufacturer, the second data includes data related to tops, pants, socks, etc. The electronic device analyzes the data related to tops, pants, socks, etc. to obtain user needs.

[0057] Optionally, electronic data can obtain multiple user needs based on second data corresponding to different preset themes.

[0058] S150, Information recommendation based on user needs.

[0059] In this embodiment of the disclosure, after obtaining user requests, the electronic device recommends information based on those requests.

[0060] Specifically, after obtaining user needs, electronic devices recommend information corresponding to those needs to the user, thereby achieving customized information recommendations for the user.

[0061] In this embodiment of the disclosure, by acquiring the user's raw data and extracting data from the raw data according to preset data extraction conditions, at least one first data is obtained. The at least one first data is associated with the preset data extraction conditions. After obtaining the at least one first data, the at least one first data is classified according to a preset theme to obtain second data corresponding to the preset theme. The second data is one or more of the at least one first data. Data analysis is performed on the second data to obtain user needs, and then information is recommended based on user needs. Thus, manufacturers can recommend useful information to users according to their actual needs, achieving accurate information recommendation and improving user experience.

[0062] Based on the above embodiments of this disclosure, obtaining the user's original data in S110 may specifically include: reading the user's link logs through a preset data reading tool to obtain the original data.

[0063] Optionally, the preset data reading tools may include Flink CDC, FireRead, etc., which are tools pre-installed in electronic devices for data reading.

[0064] Optionally, a user's link log may include links from all web pages and all apps the user browses.

[0065] Specifically, the electronic device can read the user's link logs in real time using a preset data reading tool, send the real-time read data to the electronic device, and the electronic device receives and stores the data read by the preset data reading tool.

[0066] In this embodiment of the disclosure, the electronic device can read the user's link logs using a preset data reading tool to ensure that the obtained raw data is complete and without omissions.

[0067] In this embodiment of the disclosure, after S120, the information recommendation method may further include: storing at least one first data in a first preset data storage pool to prevent at least one first data from being lost.

[0068] Optionally, the first preset data storage pool can be an HBase data storage pool.

[0069] Optionally, an HBase data storage pool has multiple partitions, each of which can store different data.

[0070] Specifically, after obtaining at least one first data, the electronic device actively pushes at least one first data to the cloud through a push tool embedded in a preset data reading tool. The cloud then automatically caches at least one first data in a first preset data storage pool, thereby storing at least one first data in the first preset data storage pool.

[0071] In this embodiment of the disclosure, the electronic device may store at least one first data in a first preset data storage pool to prevent at least one first data from being lost in the electronic device's data storage repository.

[0072] In this embodiment of the disclosure, classifying at least one first data according to a preset theme to obtain second data corresponding to the preset theme in S130 may specifically include: determining the preset theme corresponding to each data in at least one first data based on the mapping relationship between the preset theme and theme data; and determining the data corresponding to the same preset theme as the second data corresponding to the preset theme.

[0073] In this embodiment of the disclosure, the mapping relationship between preset topics and topic data can be preset and stored in a data repository.

[0074] For example, if the preset theme is "tops", then the theme data can be user account information data, brand information data of tops saved by the user, type information data of tops that the user follows or browses, etc.

[0075] Specifically, after obtaining at least one first data, the electronic device classifies each data in the at least one first data according to the mapping relationship between a preset topic and topic data, and determines the preset topic corresponding to each data in the at least one first data. At this time, the same preset topic contains one or more at least first data, and the data corresponding to the same preset topic is determined as the second data corresponding to the preset topic.

[0076] In this embodiment of the disclosure, the electronic device can classify each piece of data in at least one first piece of data according to the mapping relationship between a preset topic and topic data, so as to ensure that the obtained second data corresponding to the preset topic is complete and accurate, and to provide strong support for the next step of user demand.

[0077] In this embodiment of the disclosure, the data analysis of the second data in S140 to obtain user needs may specifically include: extracting keywords from the second data to obtain the keywords corresponding to the second data; and obtaining user needs based on the keywords.

[0078] In this embodiment of the disclosure, after the electronic device acquires the second data, it extracts keywords from the second data to obtain the keywords corresponding to the second data, and obtains user needs based on the keywords.

[0079] Specifically, after acquiring the second data, the electronic device can input the second data corresponding to different preset topics into a pre-trained machine learning model. The pre-trained machine learning model analyzes the second data and extracts the keywords of the second data, and outputs the keywords corresponding to the second data. After acquiring the keywords corresponding to the second data, the electronic device can analyze the keywords through tools such as Flink to obtain user needs.

[0080] In this embodiment of the disclosure, the electronic device can extract keywords from the second data and then analyze the keywords to obtain user needs, thereby improving the accuracy of the obtained user needs.

[0081] In this embodiment of the disclosure, the information recommendation based on user needs in S150 may specifically include: storing user needs in a second preset data storage pool; comparing and analyzing the user needs in the second preset data storage pool to obtain the user's target needs; and recommending information based on the target needs.

[0082] In this embodiment of the disclosure, the second preset data storage pool may be an HBase data storage pool, wherein the second preset data storage pool and the first preset data storage pool belong to different partitions of the HBase data storage pool.

[0083] In some embodiments of this disclosure, an electronic device can analyze second data at preset time intervals using a pre-trained machine learning model to obtain user needs within multiple different preset time intervals. The user needs within multiple different preset time intervals are stored in a second preset data storage pool. Then, within a fixed time period, the user needs within multiple different preset time intervals are extracted from the second preset data storage pool. The user needs within multiple different preset time intervals are comprehensively compared and analyzed to obtain the user's target needs within a fixed time period. Based on the target needs, message recommendations are made to the user.

[0084] The preset time interval can be a pre-set time interval for analyzing the second data, such as analyzing the second data once every other day.

[0085] A fixed time period can be a pre-set time period for analyzing user needs at multiple different times, such as one month, six months, etc.

[0086] Furthermore, the preset time interval is shorter than a fixed time period.

[0087] In other embodiments of this disclosure, the electronic device can analyze the second data in real time through a pre-trained machine learning model to obtain the user's real-time needs, determine the user's real-time needs as the user's target needs, and recommend messages to the user based on the target needs.

[0088] In this embodiment of the disclosure, the electronic device can store user needs in a second preset data storage pool, thereby facilitating different manufacturers to perform reverse lookups based on the user needs in the second preset data storage pool to confirm the accuracy of the user needs analysis. At the same time, the pre-trained machine learning model can be optimized by using the user needs in the second preset data storage pool and the data corresponding to the user needs. Furthermore, by comparing and analyzing the user needs in the second preset data storage pool, the target needs of the users can be obtained more accurately, and the messages recommended based on the target needs can be more accurate and valuable.

[0089] Based on the above embodiments of this disclosure, information recommendation based on target needs may specifically include: matching the target needs with information in a preset information database to obtain a matching degree; and using information with a matching degree greater than a preset matching threshold as target information for information recommendation.

[0090] Optionally, the preset information database can be a database composed of information that different manufacturers have pre-built and can recommend to users, which stores the information that different manufacturers want to recommend to users.

[0091] In some embodiments of this disclosure, after obtaining the user's target needs, the electronic device matches the target needs with information in a preset information database using a pre-trained machine learning model to obtain the matching degree between the target needs and each piece of information in the preset information database. Information with a matching degree greater than a preset matching threshold is used as target information, and then the target information is recommended to the user. The preset matching threshold is a pre-set threshold used to filter information in the preset information database.

[0092] In other embodiments of this disclosure, after obtaining the user's target needs, the electronic device uses a pre-trained machine learning model to match the target needs with information in a preset information database, obtains the matching degree between the target needs and each piece of information in the preset information database, and takes the information with the highest matching degree in the preset information database as the target information, and then recommends the target information to the user.

[0093] In this embodiment of the disclosure, the electronic device can obtain a matching degree by matching the target demand with information in a preset information database, and then determine the target information to be recommended to the user based on the matching degree, so that the information recommended to the user is more accurate and better meets the user's needs.

[0094] Figure 2 This is a flowchart of another information recommendation method provided in this embodiment.

[0095] like Figure 2 As shown, this information recommendation method can specifically include the following steps.

[0096] S210. Read the user's link logs using a preset data reading tool to obtain the user's raw data.

[0097] S220. According to the preset data extraction conditions, extract data from the original data to obtain at least one first data.

[0098] S230. Based on the mapping relationship between preset topics and topic data, determine the preset topic corresponding to each data in at least one set of first data.

[0099] S240. The data corresponding to the same preset theme is determined as the second data corresponding to the preset theme.

[0100] S250. Extract keywords from the second data to obtain the keywords corresponding to the second data, and obtain user needs based on the keywords.

[0101] S260. Store user requirements in the second preset data storage pool.

[0102] S270. Compare and analyze the user requirements in the second preset data storage pool to obtain the user's target requirements.

[0103] S280. Recommend information based on target needs.

[0104] In this embodiment, the electronic device can read the user's link logs using a preset data reading tool to obtain the user's raw data. After obtaining the user's raw data, data extraction is performed on the raw data according to preset data extraction conditions to obtain at least one first data. Based on the mapping relationship between preset topics and topic data, the preset topic corresponding to each data in the at least one first data is determined. Data corresponding to the same preset topic is determined as the second data corresponding to the preset topic. User needs are stored in a second preset data storage pool. The user needs in the second preset data storage pool are compared and analyzed to obtain the user's target needs. Information is recommended based on the target needs. Thus, manufacturers can recommend useful information to users according to their actual needs, achieving accurate information recommendation and improving user experience. At the same time, by storing user needs in the second preset data storage pool, user needs can be more accurately located by combining previous user needs to obtain target information. Furthermore, the accuracy of information recommendation can be further improved by optimizing pre-trained machine learning models based on previous user needs and data related to user needs.

[0105] Figure 3 This is a schematic diagram of the structure of an information recommendation device provided in an embodiment of this disclosure.

[0106] In this embodiment, the information recommendation device can be installed within an electronic device and is understood as a functional module within the aforementioned electronic device. Specifically, the electronic device can be a server or a terminal, wherein the terminal specifically includes mobile phones, computers, or tablet computers, etc., without limitation.

[0107] like Figure 3 As shown, the information recommendation device 300 may include a data acquisition module 310, a data extraction module 320, a data classification module 330, a data analysis module 340, and an information recommendation module 350.

[0108] The data acquisition module 310 can be used to acquire the user's raw data.

[0109] The data extraction module 320 can be used to extract data from the original data according to preset data extraction conditions to obtain at least one first data, wherein the at least one first data is associated with the preset data extraction conditions.

[0110] The data classification module 330 can be used to classify at least one first data according to a preset theme to obtain second data corresponding to the preset theme, wherein the second data is one or more of the at least one first data.

[0111] The data analysis module 340 can be used to perform data analysis on the second data to obtain user requirements.

[0112] The information recommendation module 350 can be used to recommend information based on user needs.

[0113] In this embodiment of the disclosure, by acquiring the user's raw data and extracting data from the raw data according to preset data extraction conditions, at least one first data is obtained. The at least one first data is associated with the preset data extraction conditions. After obtaining the at least one first data, the at least one first data is classified according to a preset theme to obtain second data corresponding to the preset theme. The second data is one or more of the at least one data. Data analysis is performed on the second data to obtain user needs, and then information is recommended based on user needs. Thus, manufacturers can recommend useful information to users according to their actual needs, achieving accurate information recommendation and improving user experience.

[0114] In some embodiments of this disclosure, the data acquisition module 310 can be specifically used to read the user's link logs using a preset data reading tool to obtain raw data.

[0115] In some embodiments of this disclosure, the information recommendation device 300 may further include a data storage module 360.

[0116] The data storage module 360 ​​can be used to store at least one first data into a first preset data storage pool to prevent at least one first data from being lost.

[0117] In some embodiments of this disclosure, the data classification module 330 may include a first determining unit 3301 and a second determining unit 3302.

[0118] The first determining unit 3301 can be used to determine the preset topic corresponding to each piece of data in at least one set of first data based on the mapping relationship between preset topics and topic data.

[0119] The second determining unit 3302 can be used to determine the data corresponding to the same preset topic as the second data corresponding to the preset topic.

[0120] In some embodiments of this disclosure, the data analysis module 340 may include a keyword extraction unit 3401 and a demand acquisition unit 3402.

[0121] The keyword extraction unit 3401 can be used to extract keywords from the second data to obtain the keywords corresponding to the second data.

[0122] The requirement acquisition unit 3402 can be used to obtain user requirements based on keywords.

[0123] In some embodiments of this disclosure, the information recommendation module 350 may include a demand storage unit 3501, a comparison and analysis unit 3502, and an information determination unit 3503.

[0124] The demand storage unit 3501 can be used to store user demands into a second preset data storage pool after receiving user demands.

[0125] The comparison analysis unit 3502 can be used to compare and analyze user needs in the second preset data storage pool to obtain the user's target needs.

[0126] The information determination unit 3503 can be used to recommend information based on target needs.

[0127] In some embodiments of this disclosure, the information determination unit 3503 may include a matching subunit and a target information determination subunit.

[0128] This matching subunit can be used to match target requirements with information in a preset information database to obtain the matching degree.

[0129] This target information determination subunit can be used to select information with a matching degree greater than a preset matching threshold as target information for information recommendation.

[0130] It should be noted that, Figure 3 The information recommendation device 300 shown can execute the various steps in the above method embodiments and realize the various processes and effects in the above method embodiments, which will not be elaborated here.

[0131] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this disclosure.

[0132] In this embodiment of the disclosure, Figure 4 The electronic devices shown can be servers or terminals, and terminals specifically include mobile phones, computers, or tablets, etc., without limitation.

[0133] like Figure 4 As shown, the electronic device may include a processor 410 and a memory 420 storing computer program instructions.

[0134] Specifically, the processor 410 may include a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this disclosure.

[0135] Memory 420 may include mass storage for information or instructions. For example, and not limitingly, memory 420 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 420 may include removable or non-removable (or fixed) media. Where appropriate, memory 420 may be internal or external to the integrated gateway device. In a particular embodiment, memory 420 is non-volatile solid-state memory. In a particular embodiment, memory 420 includes read-only memory (ROM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (Electrically Programmable ROM, EPROM), an electrically erasable programmable PROM (EEPROM), an electrically alterable ROM (EAROM), or flash memory, or a combination of two or more of these.

[0136] The processor 410 performs the steps of the information recommendation method provided in the embodiments of this disclosure by reading and executing computer program instructions stored in the memory 420.

[0137] In one example, the electronic device may also include a transceiver 430 and a bus 440. Wherein, as... Figure 4 As shown, the processor 410, memory 420 and transceiver 430 are connected via bus 440 and communicate with each other.

[0138] Bus 440 may include hardware, software, or both. For example, and not limitingly, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industrial Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a MicroChannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable buses, or a combination of two or more of these. Where appropriate, bus 440 may include one or more buses.

[0139] This disclosure also provides a computer-readable storage medium that can store a computer program that, when executed by a processor, causes the processor to implement the information recommendation method provided in this disclosure.

[0140] The aforementioned storage medium may, for example, include a memory 420 containing computer program instructions, which can be executed by a processor 410 of an electronic device to perform the information recommendation method provided in the embodiments of this disclosure. Optionally, the storage medium may be a non-transitory computer-readable storage medium, such as a ROM, random access memory (RAM), compact disc ROM (CD-ROM), magnetic tape, floppy disk, and optical data storage device.

[0141] The above description is merely a specific embodiment of this disclosure, enabling those skilled in the art to understand or implement it. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this disclosure. Therefore, this disclosure is not to be limited to the embodiments described herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An information recommendation method, characterized in that, include: Obtain the user's raw data; According to preset data extraction conditions, the original data is extracted to obtain at least one first data, wherein the at least one first data is associated with the preset data extraction conditions; The at least one first data is classified according to a preset theme to obtain second data corresponding to the preset theme, wherein the second data is one or more of the at least one first data; the preset theme is a theme corresponding to the preset data extraction conditions; Data analysis is performed on the second set of data to obtain user requirements; Information recommendations are made based on the user's needs. The information recommendation based on the user's needs includes: The user's needs are matched with information in a preset information database to obtain the matching degree; The information with a matching degree greater than a preset matching threshold is used as target information for information recommendation.

2. The method according to claim 1, characterized in that, The acquisition of the user's raw data includes: The user's link logs are read using a preset data reading tool to obtain the raw data.

3. The method according to claim 1, characterized in that, After extracting data from the original data according to preset data extraction conditions to obtain at least one first data point, the method further includes: The at least one first data is stored in a first preset data storage pool to prevent the at least one first data from being lost.

4. The method according to claim 1, characterized in that, The step of classifying the at least one first data according to a preset theme to obtain second data corresponding to the preset theme includes: Based on the mapping relationship between the preset topic and the topic data, the preset topic corresponding to each data in the at least one first data is determined; The data corresponding to the same preset theme is determined as the second data corresponding to the preset theme.

5. The method according to claim 1, characterized in that, The step of performing data analysis on the second data to obtain user needs includes: Keyword extraction is performed on the second data to obtain the keywords corresponding to the second data; Based on the keywords, the user needs are obtained.

6. The method according to claim 1, characterized in that, The information recommendation based on the user's needs includes: The user requirements are stored in the second preset data storage pool; By comparing and analyzing the user requirements in the second preset data storage pool, the user's target requirements are obtained; The information recommendation is based on the stated target requirements.

7. An information recommendation device, characterized in that, include: The data acquisition module is used to acquire the user's raw data; A data extraction module is used to extract data from the original data according to preset data extraction conditions to obtain at least one first data, wherein the at least one first data is associated with the preset data extraction conditions; A data classification module is used to classify the at least one first data according to a preset theme to obtain second data corresponding to the preset theme, wherein the second data is one or more of the at least one first data; the preset theme is a theme corresponding to the preset data extraction conditions; The data analysis module is used to perform data analysis on the second data to obtain user requirements; The information recommendation module is used to recommend information based on the user's needs; The information recommendation module includes an information determination unit, which includes a matching subunit and a target information determination subunit. The matching subunit is used to match the user's needs with information in a preset information database to obtain a matching degree. The target information determination subunit is used to select information with a matching degree greater than a preset matching threshold as target information for information recommendation.

8. An electronic device, characterized in that, include: processor; Memory, used to store executable instructions; The processor is configured to read the executable instructions from the memory and execute the executable instructions to implement the information recommendation method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The storage medium stores a computer program that, when executed by a processor, causes the processor to implement the information recommendation method according to any one of claims 1-6.

Images