Method, device and equipment for searching and computer readable storage medium

Abstract

The embodiment of the invention provides a method, device and equipment for searching and a computer readable storage medium. The method can comprise the steps that at least one candidate object matched with a searching request is determined; obtaining a sorting factor of the candidate object in the at least one candidate object, wherein the sorting factor is at least used for indicating at least one item of geographic position information corresponding to the candidate object and feature information of a category corresponding to the candidate object; and determining a search result for the search request based on the sorting factor of the at least one candidate object. Therefore, the sorting factor is used as a data basis, and the search process is completed from the search dimension of the candidate object. On the premise that the expected search result is obtained, the search complexity and the data volume are reduced, and the method has a wider application prospect.

Description

Technical Field

[0001] The exemplary embodiments disclosed herein relate generally to the field of computers, and more specifically to methods, apparatus, devices, and computer-readable storage media for searching. Background Technology

[0002] Search scenarios are diverse. Taking in-store service search as an example, it currently faces many technical challenges and limitations. These include issues such as the repetitiveness of search results and the complexity of the search process. Relevant technologies are currently unable to solve these problems. Summary of the Invention

[0003] In a first aspect of this disclosure, a method for searching is provided. The method may include: determining at least one candidate object matching a search request; obtaining a ranking factor for the candidate object from the at least one candidate object, the ranking factor indicating at least one of geographic location information corresponding to the candidate object and feature information of a category corresponding to the candidate object; and determining a search result for the search request based on the ranking factor of the at least one candidate object.

[0004] In a second aspect of this disclosure, an apparatus for searching is provided. The apparatus may include: a candidate object determination module configured to determine at least one candidate object matching a search request; a ranking factor acquisition module configured to acquire a ranking factor for one of the candidate objects, the ranking factor indicating at least one of geographic location information corresponding to the candidate object and feature information of a category corresponding to the candidate object; and a search result determination module configured to determine a search result for the search request based on the ranking factor of the at least one candidate object.

[0005] In a third aspect of this disclosure, an electronic device is provided. The electronic device includes at least one processing unit; and at least one memory coupled to the at least one processing unit and storing instructions for execution by the at least one processing unit, the instructions causing the electronic device to perform the method of the first aspect of this disclosure when executed by the at least one processing unit.

[0006] In a fourth aspect of this disclosure, a computer-readable storage medium is provided. This computer-readable storage medium stores a computer program that can be executed by a processor to perform the method according to a first aspect of this disclosure.

[0007] It should be understood that the content described in this summary section is not intended to limit the key or essential features of the embodiments of this disclosure, nor is it intended to restrict the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0008] The above and other features, advantages, and aspects of various implementations of this disclosure will become more apparent in the following detailed description, taken in conjunction with the accompanying drawings. In the drawings, the same or similar reference numerals denote the same or similar elements, wherein:

[0009] Figure 1 A schematic diagram of an example environment in which embodiments of the present disclosure can be implemented is shown;

[0010] Figure 2 A flowchart of a search process according to some embodiments of the present disclosure is shown;

[0011] Figure 3 A schematic diagram of an interactive interface according to some embodiments of the present disclosure is shown;

[0012] Figure 4 A schematic diagram illustrating the search principle for searching according to some embodiments of the present disclosure is shown;

[0013] Figure 5 A schematic diagram of the overall architecture of a search method according to some embodiments of the present disclosure is shown;

[0014] Figure 6 A block diagram of an apparatus for searching according to some embodiments of the present disclosure is shown; and

[0015] Figure 7 A block diagram of an electronic device in which one or more embodiments of the present disclosure may be implemented is shown. Detailed Implementation

[0016] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0017] In the description of embodiments of this disclosure, the term "comprising" and similar terms should be understood as open-ended inclusion, i.e., "including but not limited to". The term "based on" should be understood as "at least partially based on". The term "one embodiment" or "the embodiment" should be understood as "at least one embodiment". The term "some embodiments" should be understood as "at least some embodiments". Other explicit and implicit definitions may also be included below.

[0018] In this document, unless explicitly stated otherwise, performing a step in response to A does not mean that the step is performed immediately after A, but may include one or more intermediate steps.

[0019] It is understood that the data involved in this technical solution (including but not limited to the data itself, the acquisition, use, storage or deletion of the data) shall comply with the requirements of relevant laws, regulations and related provisions.

[0020] It is understood that before using the technical solutions disclosed in the various embodiments of this disclosure, relevant users should be informed of the type, scope of use, and usage scenarios of the information involved in this disclosure through appropriate means in accordance with relevant laws and regulations, and authorization should be obtained from the relevant users. Among them, relevant users may include any type of rights holder, such as individuals, enterprises, and groups.

[0021] For example, in response to receiving an active request from a user, a prompt message is sent to the relevant user to clearly inform the user that the requested operation will require obtaining and using the user's information, thereby enabling the relevant user to choose whether to provide information to the software or hardware such as the electronic device, application, server, or storage medium that performs the operation of the technical solution disclosed herein based on the prompt message.

[0022] As an optional but non-restrictive implementation, in response to a user's active request, a prompt message can be sent to the user, such as a pop-up window, where the prompt message can be presented in text format. Furthermore, the pop-up window can also include a selection control allowing the user to choose "agree" or "disagree" to provide information to the electronic device.

[0023] It is understood that the above notification and user authorization process are merely illustrative and do not constitute a limitation on the implementation of this disclosure. Other methods that comply with relevant laws and regulations may also be applied to the implementation of this disclosure.

[0024] As used in this paper, the term "model" refers to a model that learns the relationship between inputs and outputs from training data, enabling it to generate corresponding outputs for a given input after training. Model generation can be based on machine learning techniques. Deep learning is a machine learning algorithm that processes inputs and provides corresponding outputs using multiple layers of processing units. A neural network model is an example of a deep learning-based model. In this paper, "model" may also be referred to as a "machine learning model," "learning model," "machine learning network," or "learning network," and these terms are used interchangeably.

[0025] Typically, in search scenarios, especially when the search target is in-store services, the search results are mostly based on the store where the search result is located. In-store services refer to services that require customers to personally visit a location designated by the merchant to enjoy or complete. For example, in-store services may include medical services such as physical examinations and doctor visits, as well as beauty and hair services, car repair and maintenance services, etc.

[0026] Taking a physical examination search request as an example, the search results obtained by related technologies are usually based on the physical examination products of a specific hospital or a specific physical examination center. In other words, the search results are typically displayed based on the store where the target product is located, lacking the ability to search based on the target product itself. Secondly, if multiple stores have the same target product, duplicate results will occur. Furthermore, building a search system involves a significant amount of in-house development work. From a technical perspective, implementing a search scenario is a massive and complex undertaking.

[0027] Figure 1 A schematic diagram of an example environment 100 in which embodiments of the present disclosure can be implemented is shown. For example... Figure 1 As shown, environment 100 may include electronic device 110.

[0028] Electronic device 110 receives search request 102. By parsing search request 102, at least one candidate object matching the search request is identified. Through data platform 112, electronic device 110 can obtain the ranking factor of each candidate object. For example, the ranking factor can reflect the degree of matching between the candidate object and the search request from multiple dimensions such as the candidate object's location, evaluation information, and popularity information. Finally, electronic device 110 can rank multiple candidate objects according to their matching degree, thereby selecting a specified number of candidate objects as the search results for the search request. It is easy to understand that the ranking factors of different candidate objects are stored in data platform 112. Furthermore, the ranking factors can be dynamically updated. For example, data can be updated hourly or daily. Thus, when conducting a search, candidate objects can be used as the search dimension, and appropriate search results can be determined based on the ranking factors of the candidate objects. In other words, the constructed ranking factors serve as the data foundation, and the search process is completed from the search dimension of candidate objects. While meeting the requirement of obtaining the expected search results, the complexity and data volume of the search are reduced, which has a broader application prospect.

[0029] The following description will focus on exemplary embodiments of the present disclosure with reference to the accompanying drawings. Figure 1A schematic diagram of an example environment 100 in which embodiments of the present disclosure can be implemented is shown. In environment 100, various client applications, such as shopping applications, review applications, etc., can be installed on electronic device 110. Electronic device 110 is configured to process search requests to obtain search results.

[0030] In some embodiments of this disclosure, the search engine performing the search may be deployed on a remote device. Electronic device 110 may communicate with the remote device (e.g., via network communication) to utilize a search engine stored thereon to perform search tasks. Search requests may be received by electronic device 110 and sent to the remote device. In some embodiments, the search engine may also be deployed partially or entirely locally on electronic device 110 and run by electronic device 110 to perform search tasks in response to search requests. Embodiments of this disclosure do not impose specific limitations in this regard.

[0031] Electronic device 110 can be any type of mobile terminal, fixed terminal, or portable terminal, including mobile phones, desktop computers, laptop computers, notebook computers, netbook computers, tablet computers, media computers, multimedia tablets, 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 and peripherals of these devices or any combination thereof. Remote devices may include, for example, computing systems / servers, such as mainframes, edge computing nodes, computing devices in cloud environments, virtual machines, etc.

[0032] It should be understood that the structure and function of environment 100 are described for illustrative purposes only and do not imply any limitation on the scope of this disclosure.

[0033] Figure 2 A flowchart of a search process 200 according to some embodiments of the present disclosure is shown. Process 200 may be implemented in environment 100. Process 200 may be implemented at electronic device 110. In some embodiments, electronic device 110 may utilize a search engine running locally or on a remote device to determine search results.

[0034] In box 201, electronic device 110 identifies at least one candidate object that matches the search request.

[0035] A search request can refer to keywords entered by the user, such as "health checkup products" or "car interior cleaning." Alternatively, a search request can also be a category selected by the user on the search interface. For example, electronic device 110 displays multiple category boxes in its user interface, each associated with multiple objects. Exemplarily, the category boxes can be categorized by level, including primary category boxes, secondary category boxes, etc. A search request can correspond to an in-store service scenario. Figure 3 The diagram shown is a schematic representation of an interactive interface 300 according to some embodiments of the present disclosure. "Health Services" can be used as a primary category box, while "Eye Health," "Beauty Health," and "Physical Examination," etc., can be used as secondary category boxes. Search requests can be selections from category boxes, or they can be specific text content or keywords corresponding to voice commands.

[0036] Figure 4 The diagram illustrates a search principle 400 for searching according to some embodiments of this disclosure. Electronic device 110 performs a search in data platform 112 based on a received search request 102 to determine candidate objects. Typically, there can be multiple candidate objects found. In data platform 112, a database containing relevant information for all objects can be constructed. For example, data platform 112 can obtain relevant data from a specified data source 401, such as product data, store data corresponding to the product, etc. Product data can correspond to candidate objects in the embodiments. For example, taking a physical examination product as a candidate object, and the physical examination product including three different physical examination products as an example. The first physical examination product can be an employee pre-employment physical examination, the second physical examination product can be a health examination for men, and the third physical examination product can be a health examination for women. For each health checkup product, the data platform can include information across multiple dimensions, such as the product's identifier, its corresponding category, the location of the hospital or health checkup center providing the product, user feedback on the product or the hospital or health checkup center providing it, the number of orders for the product, and its price. This information can be stored as data, serving as a sorting factor 402.

[0037] In box 202, electronic device 110 acquires a ranking factor for at least one candidate object among at least the candidate objects. The ranking factor is used to indicate at least one of the geographical location information corresponding to the candidate object and the feature information of the category corresponding to the candidate object.

[0038] After identifying candidate objects matching the search request, electronic device 110 can obtain a ranking factor 402 for each candidate object from data platform 112. The ranking factor 402 can be used to indicate at least one of the following: the geographical location information corresponding to the candidate object and the feature information of the candidate object's corresponding category. For example, taking a medical examination product as a candidate object, the location of the hospital or medical examination center providing the product, the category corresponding to the product, user feedback on the product or the hospital or medical examination center providing it, and the number of orders for the product can all be used as ranking factors for the candidate object. The location information of the hospital or medical examination center providing the product can correspond to the geographical location information of the candidate object. The category of the product, user feedback, and the number of orders can all be used as feature information of the candidate object's corresponding category. The category of the medical examination product could correspond to, for example, pre-employment medical examination or general health checkup.

[0039] In box 203, electronic device 110 determines the search results for a search request based on a ranking factor for at least one candidate object.

[0040] Combination Figure 4 As shown, search results can be determined based on the ranking factor 402 for each candidate object. That is, candidate objects can be ranked based on their corresponding geographic location information and the feature information of their corresponding categories, resulting in a ranking result 403. Finally, search results are determined based on the ranking result 403. Thus, the distance between a candidate object and a specified location can be determined based on the geographic location information of the candidate object. The specified location can be the user's current location or a selected location. For example, the electronic device 110 can employ Location Based Service (LBS) to determine the distance between a candidate object and a specified location using the geographic location information of the candidate object. Furthermore, the feature information of the candidate object's corresponding category can make the search results more closely match the search request. In addition, it can simplify the amount of data for the ranking factor and reduce the resource consumption of the search process.

[0041] In some embodiments, in response to the feature information corresponding to the candidate object's category including at least one of the candidate object's evaluation information and the candidate object's popularity information, and wherein the electronic device 110 determines the search results based on the ranking factor of at least one candidate object by: for each candidate object of the at least one candidate object, determining a first ranking parameter based on the geographical location information corresponding to the candidate object; determining a second ranking parameter based on the candidate object's evaluation information; and / or determining a third ranking parameter based on the candidate object's popularity information; determining a ranking score for the candidate object based on the first ranking parameter, the second ranking parameter, and the third ranking parameter; and determining the search results for the search request based on the ranking score of the at least one candidate object.

[0042] The ranking factors can be stored in the data platform 112. For example, referring to the ranking factor examples in Table 1, taking a pre-employment medical examination package as a candidate, the ranking factors for the pre-employment medical examination package can include name information, category information, store information, order quantity information, review information, etc., corresponding to the identifier of the pre-employment medical examination package. For example, at least one of the order quantity information and review information can be used as the feature information corresponding to the category of the candidate object.

[0043] Table 1 Examples of ranking factors

[0044]

[0045]

[0046] Based on the geographical location information of the candidate object, the distance d between the candidate object and the specified location can be determined. Then, based on the first weight parameter and the distance d, the first ranking parameter can be determined.

[0047] Evaluation information can be in the form of ratings or in text form. For text-based evaluations, natural language processing techniques can be used to convert them into ratings. Based on the second weight parameter and the rating-based evaluation, the second ranking parameter can be determined.

[0048] The popularity information corresponding to a candidate object can be the number of times the candidate object has been ordered within a certain period, or the number of times the candidate object has been followed or recommended. Based on the popularity information corresponding to the third weight parameter and the candidate object, the third ranking parameter can be determined.

[0049] For example, the first weight parameter can be set to 0.5, the second weight parameter can be set to 0.3, and the third weight parameter can be set to 0.3. These weight parameter settings are merely illustrative and do not limit the specific values. Based on the first, second, and third ranking parameters of a candidate object, its ranking score can be determined. Furthermore, based on the ranking score of each candidate object, the ranking result of all candidate objects can be obtained. Based on the ranking result, a specified number of candidate objects can be selected for recommendation. Alternatively, the recommendation order of all candidate objects can be determined based on the ranking result.

[0050] In some embodiments, the first sorting parameter, the second sorting parameter, and the third sorting parameter are parameters mapped to the same numerical range.

[0051] The first sorting parameter indicates distance, typically ranging from hundreds to thousands of meters. The second sorting parameter corresponds to evaluation information, usually ranging from 0 to 5, 0 to 10, or 0 to 100 depending on the evaluation score. The third sorting parameter corresponds to popularity information, which can include sales volume, number of views, etc., often reaching thousands, tens of thousands, or even more. In other words, there are dimensional differences between the first, second, and third sorting parameters. Therefore, it is necessary to map the first, second, and third sorting parameters to the same numerical range to eliminate these dimensional differences.

[0052] For example, the mapping result for the first sorting parameter can start from an initial value (usually 1). As the distance increases, the mapping result gradually decays to 0. This means that candidates closer to the specified location will receive a higher first sorting parameter (between [0,1]). The farther away from the specified location, the lower the first sorting parameter of the candidate. The target location can be the user's current location or a location specified by the user, etc.

[0053] For the second sorting parameter, the mapping is as follows:

[0054]

[0055] In expression (1), sortScore can represent the (normalized) second sorting parameter, and x can be the evaluation information in the form of the current candidate object's score. min It can be the minimum value in the rating information, x max It can be the maximum value in the rating information. Therefore, if the rating score is between 0 and 5, or between 0 and 100, it can be mapped to the range [0,1].

[0056] For the third sorting parameter, the mapping is as follows:

[0057]

[0058] In expression (2), sortSales can represent the (normalized) third sorting parameter. If sales volume represents popularity information, then S1 can correspond to the sales volume of the first time period (e.g., the past 7 days), S2 can correspond to the sales volume of the second time period (e.g., the past 30 days), and S3 can correspond to the total sales volume. Thus, the third sorting parameter can be mapped to the range [0,1].

[0059] The above process can eliminate the dimensions between different pieces of information.

[0060] In some embodiments, in response to the fact that the geographic locations indicated by the geographic location information corresponding to the candidate object are multiple, distance information between each geographic location and the specified location is determined; based on the distance information, the target geographic location is determined from the multiple geographic location information; and the geographic location information corresponding to the candidate object is determined as the indicated target geographic location.

[0061] Taking a health checkup product as an example, as shown in Table 1, the same health checkup product may exist in multiple health checkup stores or hospitals. Each health checkup store or hospital corresponds to a geographical location. Therefore, for the same health checkup product, the geographical location information indicates multiple geographical locations. In this case, the electronic device 110 can determine the distance information between each geographical location and the specified location.

[0062] Distance information can be determined in the following way. First, the electronic device 110 determines a specified location, which can be the user's current location or a location set by the user. The electronic device 110 can then obtain the geographic location information of the specified location. Next, based on the geographic location information of the specified location and multiple geographic locations of candidate objects, the electronic device 110 determines the distance between the specified location and multiple locations of candidate objects. For example, Table 1 contains a first store and a second store, each with a distance to a specified location (e.g., the user's current location). The device selects the one with the closest distance as the selection result. For example, in the example in Table 1, if the first store is closest to the specified location, then the location information of the first store can be used as the location information of the candidate object. Conversely, if the second store is closest to the specified location, then the location information of the second store can be used as the location information of the candidate object. In other words, for the current health checkup product, even if multiple stores can provide the corresponding service, only one store will be selected when determining the search results. This avoids the situation where the same health checkup product appears multiple times in different stores in the search results.

[0063] In some embodiments, in response to the presence of multiple search results, the electronic device 110 determines a set of categories based on the category corresponding to each search result; and in response to a selection instruction for at least one target category in the set of categories, performs filtering on the multiple search results to obtain filtering results.

[0064] Taking a keyword search as an example, multiple search results can be obtained based on a keyword search. For example, if the keyword is "physical examination," multiple search results containing the keyword or with the same or similar meanings will typically be obtained. The storage structure shown in Table 1, as previously mentioned, can correspond to each search result. That is, for each search result, there exists a corresponding category. Therefore, the electronic device 110 can construct a category set based on the categories corresponding to the search results.

[0065] The category set can be displayed on the user interface. This enables the ability to infer the filtering scope based on search results. In other words, a category combination can be constructed based on the category corresponding to each search result. Furthermore, the constructed category set can be provided to the user for selection on the user interface. Figure 3 Examples shown include eye health, beauty health, and physical examinations, which could be constructed category sets. Therefore, since the category set is derived from the search results, it is not static but may dynamically change with each different search result. In response to the user's received instruction to select a target category, the electronic device 110 can display the search results corresponding to the target category on the interactive search results page.

[0066] In some embodiments, the electronic device 110 obtains the number of target categories; constructs a query thread corresponding to the number; and performs filtering on multiple search results based on the query thread.

[0067] In response to a target category selection instruction, electronic device 110 can obtain the number of target categories. For each target category, a query thread can be constructed. That is, if there are n target categories, n query threads are constructed, where n is a positive integer. Executing the query threads yields the search results for each target category. This allows for the filtering of search results.

[0068] In some embodiments, the ranking factor of the candidate object is constructed in the following manner: determining the field information of the candidate object, the field information including first field information indicating fixed content and second field information indicating changed content; obtaining content information related to the second field information; and merging and storing the field information and the content information related to the field information as the ranking factor of the candidate object.

[0069] The ranking factors for candidate objects can be constructed using electronic device 110. These ranking factors can be stored in the data platform in the form of Table 1. The field information may include a first field indicating fixed content and a second field indicating variable content. The first field indicating fixed content may be a field indicating candidate object identification, name, and category, etc. The second field indicating variable content may be a field such as store rating or sales data.

[0070] The content information can be obtained from a specified database. For example, the specified database could be a store rating statistics database, a sales data statistics database, etc. Data from the specified database can be inserted into the corresponding positions in Table 1 to obtain the complete content of Table 1. This completes the construction of the sorting factors.

[0071] In some embodiments, the electronic device 110 may also update at least one piece of information in the sorting factors in response to an update request for the sorting factors.

[0072] Update requests can be triggered by the magnitude of a data change or by a change over time. For example, if the rating changes by more than 0.5 points or more than 1 point, an update request can be triggered. Time-triggered updates could be triggered every 12 hours or every 24 hours. Update requests can also be initiated proactively by maintenance personnel, such as when a new store is added, requiring updates to the new store's information.

[0073] This ensures that the data corresponding to the ranking factors is updated in a timely manner, thereby improving the accuracy of search result determination.

[0074] Figure 5 A schematic diagram of the overall architecture 500 of a search method according to some embodiments of the present disclosure is shown. A data platform 112 is used for data collection. In the current embodiment, the data collected by the data platform 112 includes object data and store data. Exemplarily, object data may include information such as object name and object category. Objects may correspond to in-store services, such as physical examination services, beauty and hairdressing services, and car repair and maintenance services. Store data may include information such as store name and location. For each store, the store can be associated with an object.

[0075] The operations unit 501 can configure data. For example, it can configure search components and the category range of fixed object components. For instance, configuring search components typically refers to modules or toolsets used to implement search functionality. These components include functions such as configuration, indexing, querying, and result processing. Furthermore, by fixing the category range of object components, it ensures that the searched and displayed objects conform to a specific category range. The configured search components and the fixed category range of object components can be stored in a specified manner, such as in a MySQL database, and in conjunction with a Redis cache. For example, when electronic device 110 performs a query, it first queries the Redis cache. If the product information exists in the cache, it is returned directly; if it does not exist in the cache, it queries the MySQL database, retrieves the data, returns it to the user, and caches the result in Redis for future requests. In addition, data in the data platform 112 can also be processed on the operations unit 501. For example, the operations unit 501 can combine data from the MySQL database and extract relevant data as shown in Table 1 above. The data is then stored in a structured manner according to the structure of Table 1. In addition, the 501 operation platform can normalize some data based on mapping rules, such as normalizing sales data and normalizing comment data.

[0076] Electronic device 110 executes a corresponding search based on the received search request. The search request may include keyword search or category search. Taking keyword search as an example, multiple search results obtained from keywords can be filtered and inferred. That is, a first search can be performed based on keywords to obtain multiple results. A category set is constructed based on the categories of the search results. The category set can be displayed to the user on the interactive interface. The user's selection of the category set determines the target category selected by the user. Thus, a search is performed on the second data platform based on the target category. The search process can be performed by constructing a query statement to query based on the target category. For the results obtained from the query, the final search result can be determined based on the ranking factor. For category search, its execution logic is the same as the search process after determining the target category in keyword search. The first query can correspond to the query process of the aforementioned embodiments, and the second query can correspond to the query process of related technologies. The second data platform can be a data platform in the electronic device, and the data can be shared with the operation terminal or can be partial data obtained from the MySQL database of the operation terminal.

[0077] Figure 6 A block diagram of a search apparatus 600 according to some embodiments of the present disclosure is shown. Apparatus 600 may be implemented in a remote device and / or electronic device 110. Various modules / components in apparatus 600 may be implemented by hardware, software, firmware, or any combination thereof.

[0078] The apparatus 600 includes a candidate object determination module 601 configured to determine at least one candidate object matching a search request; a ranking factor acquisition module 602 configured to acquire a ranking factor of the candidate object among the at least one candidate object, the ranking factor being used to indicate at least one of the geographical location information corresponding to the candidate object and the feature information of the category corresponding to the candidate object; and a search result determination module 603 configured to determine the search result for the search request based on the ranking factor of the at least one candidate object.

[0079] In some embodiments, in response to the feature information corresponding to the category of the candidate object including at least one of the evaluation information of the candidate object and the popularity information corresponding to the candidate object, the search result determination module 603 may include a first ranking parameter determination submodule, which is configured to determine a first ranking parameter based on the geographical location information corresponding to the candidate object for each candidate object of at least one candidate object; a second ranking parameter determination submodule, which is configured to determine a second ranking parameter based on the evaluation information of the candidate object; and / or a third ranking parameter determination submodule, which is configured to determine a third ranking parameter based on the popularity information corresponding to the candidate object; a ranking score determination submodule, which is configured to determine the ranking score of the candidate object based on the first ranking parameter, the second ranking parameter and the third ranking parameter; and a search result determination execution submodule, which is configured to determine the search results for the search request based on the ranking scores of at least one candidate object.

[0080] In some embodiments, the first sorting parameter, the second sorting parameter, and the third sorting parameter are parameters mapped to the same numerical range.

[0081] In some embodiments, the apparatus 600 further includes a geographic location information determination module, which is configured to determine distance information between each geographic location and a specified location in response to the geographic location information indicating multiple geographic locations corresponding to candidate objects; determine a target geographic location from the multiple geographic locations based on the distance information; and determine the geographic location information corresponding to candidate objects as indicating the target geographic location.

[0082] In some embodiments, in response to multiple search results, the apparatus 600 reverse module is configured to determine a set of categories based on the category corresponding to each search result; and in response to a selection instruction for at least one target category in the set of categories, to perform filtering on the multiple search results to obtain filtering results.

[0083] In some embodiments, the reverse-engineering module is further configured to obtain the number of target categories; construct a query thread corresponding to the number; and perform filtering on multiple search results based on the query thread.

[0084] In some embodiments, a ranking factor construction module is further included, which is configured to: determine field information of candidate objects, the field information including first field information indicating fixed content and second field information indicating changed content; obtain content information related to the second field information; and merge and store the field information and the content information related to the field information as ranking factors of candidate objects.

[0085] In some embodiments, an information update module is further included, which is configured to update at least one piece of information in the ranking factors in response to an update request for the ranking factors.

[0086] The units included in device 600 can be implemented in various ways, including software, hardware, firmware, or any combination thereof. In some embodiments, one or more units may be implemented using software and / or firmware, such as machine-executable instructions stored on a storage medium. In addition to or as an alternative to machine-executable instructions, some or all of the units in device 600 may be implemented at least partially by one or more hardware logic components. By way of example and not limitation, exemplary types of hardware logic components that may be used include field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoCs), complex programmable logic devices (CPLDs), and so on.

[0087] Figure 7 A block diagram of an electronic device 700 in which one or more embodiments of the present disclosure may be implemented is shown. It should be understood that... Figure 7 The electronic device 700 shown is merely exemplary and should not be construed as limiting the functionality and scope of the embodiments described herein. Figure 7 The electronic device 700 shown can be used to achieve Figure 1 Electronic devices 110.

[0088] like Figure 7 As shown, electronic device 700 is in the form of a general-purpose electronic device. Components of electronic device 700 may include, but are not limited to, one or more processors or processing units 710, memory 720, storage device 730, one or more communication units 740, one or more input devices 750, and one or more output devices 760. Processing unit 710 may be a physical or virtual processor and is capable of performing various processes according to programs stored in memory 720. In a multiprocessor system, multiple processing units execute computer-executable instructions in parallel to improve the parallel processing capability of electronic device 700.

[0089] Electronic device 700 typically includes multiple computer storage media. Such media can be any available media accessible to electronic device 700, including but not limited to volatile and non-volatile media, removable and non-removable media. Memory 720 can be volatile memory (e.g., registers, cache, random access memory (RAM)), non-volatile memory (e.g., read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory), or some combination thereof. Storage device 730 can be a removable or non-removable medium and can include machine-readable media, such as flash drives, disks, or any other media that can be used to store information and / or data (e.g., training data for training) and can be accessed within electronic device 700.

[0090] Electronic device 700 may further include additional removable / non-removable, volatile / non-volatile storage media. Although not explicitly stated... Figure 7 As shown, disk drives for reading from or writing to removable, non-volatile disks (e.g., "floppy disks") and optical disk drives for reading from or writing to removable, non-volatile optical disks can be provided. In these cases, each drive can be connected to a bus (not shown) via one or more data media interfaces. Memory 720 may include computer program product 725 having one or more program modules configured to perform various methods or actions of various embodiments of this disclosure.

[0091] The communication unit 740 enables communication with other electronic devices via a communication medium. Additionally, the functionality of the components of the electronic device 700 can be implemented using a single computing cluster or multiple computing machines capable of communicating via communication connections. Therefore, the electronic device 700 can operate in a networked environment using logical connections to one or more other servers, network personal computers (PCs), or another network node.

[0092] Input device 750 can be one or more input devices, such as a mouse, keyboard, trackball, etc. Output device 760 can be one or more output devices, such as a monitor, speaker, printer, etc. Electronic device 700 can also communicate with one or more external devices (not shown) via communication unit 740 as needed. These external devices include storage devices, display devices, etc., and can communicate with one or more devices that enable user interaction with electronic device 700, or with any device that enables electronic device 700 to communicate with one or more other electronic devices (e.g., network card, modem, etc.). Such communication can be performed via input / output (I / O) interface (not shown).

[0093] According to an exemplary implementation of this disclosure, a computer-readable storage medium is provided that stores one or more computer instructions, wherein the one or more computer instructions are executed by a processor to implement the methods described above. According to an exemplary implementation of this disclosure, a computer program product is also provided, which is tangibly stored on a non-transient computer-readable medium and includes computer-executable instructions that are executed by a processor to implement the methods described above.

[0094] Various aspects of this disclosure are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products implemented according to this disclosure. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0095] These computer-readable program instructions can be provided to a processing unit of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processing unit of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner. Thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0096] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions that execute on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0097] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction, which contains one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0098] Various implementations of this disclosure have been described above. The foregoing description is exemplary and not exhaustive, nor is it limited to the disclosed implementations. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described implementations. The terminology used herein is chosen to best explain the principles, practical applications, or improvements to technology in the market, or to enable others skilled in the art to understand the implementations disclosed herein.

Claims

1. A method for searching, comprising: Identify at least one candidate object that matches the search request; Obtain a ranking factor for a candidate object among the at least one candidate object, wherein the ranking factor is used to indicate at least one of the geographical location information corresponding to the candidate object and the feature information of the category corresponding to the candidate object; as well as Based on the ranking factors of the at least one candidate object, the search results for the search request are determined.

2. The method according to claim 1, wherein the feature information of the category corresponding to the candidate object includes at least one of the evaluation information of the candidate object and the popularity information corresponding to the candidate object, and wherein determining the search results for the search request based on the ranking factor of the at least one candidate object includes: For each of the at least one candidate object Based on the geographical location information corresponding to the candidate objects, the first sorting parameter is determined; Based on the evaluation information of the candidate objects, the second ranking parameter is determined; and / or Based on the popularity information corresponding to the candidate objects, the third sorting parameter is determined; The ranking score of the candidate object is determined based on the first ranking parameter, the second ranking parameter and / or the third ranking parameter; as well as Based on the ranking scores of the at least one candidate object, the search results for the search request are determined.

3. The method according to claim 2, wherein the first sorting parameter, the second sorting parameter and the third sorting parameter are parameters mapped to the same numerical range.

4. The method according to claim 1, further comprising: In response to the fact that there are multiple geographic locations indicated by the geographic location information corresponding to the candidate object, the distance information between each geographic location and the specified location is determined; Based on the distance information, the target geographical location is determined from a plurality of geographical locations; as well as The geographical location information corresponding to the candidate object is determined as the indicator of the target geographical location.

5. The method according to claim 1, wherein the search results are multiple, and the method further comprises: Based on the category corresponding to each search result, determine the category set; as well as In response to a selection instruction for at least one target category in the set of categories, filtering is performed on a plurality of the search results to obtain filtering results.

6. The method of claim 5, wherein filtering the plurality of search results comprises: Obtain the number of the target categories; Construct a query thread corresponding to the stated quantity; as well as Based on the query thread, filtering is performed on multiple search results.

7. The method according to claim 1, wherein the ranking factor of the candidate object is constructed in the following manner: Determine the field information of the candidate object, the field information including a first field information indicating fixed content and a second field information indicating changed content; Obtain content information related to the second field information; and The field information and related content information are combined and stored as the sorting factor for the candidate objects.

8. The method according to claim 7, further comprising: In response to an update request for the sorting factors, at least one piece of information in the sorting factors is updated.

9. A device for searching, comprising: The candidate object determination module is configured to determine at least one candidate object that matches the search request; The ranking factor acquisition module is configured to acquire the ranking factor of the candidate object among the at least one candidate object, wherein the ranking factor is used to indicate at least one of the geographical location information corresponding to the candidate object and the feature information of the category corresponding to the candidate object; as well as The search result determination module is configured to determine the search results for the search request based on the ranking factors of the at least one candidate object.

10. An electronic device, comprising: At least one processing unit; as well as At least one memory, coupled to the at least one processing unit and storing instructions for execution by the at least one processing unit, which, when executed by the at least one processing unit, cause the electronic device to perform the method according to any one of claims 1 to 8.

11. A computer-readable storage medium having a computer program stored thereon, the computer program being executable by a processor to implement the method according to any one of claims 1 to 8.

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