Search method, apparatus, device, storage medium and computer program product
By responding to search terms from multiple search engines on a new page and filtering search results based on relevance, the problem of cumbersome operation and low efficiency in existing technologies is solved, achieving automatic filtering and efficient searching.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- BEIJING HONGTENG INTELLIGENT TECH CO LTD
- Filing Date
- 2024-12-10
- Publication Date
- 2026-06-12
AI Technical Summary
In existing technologies, when the search results obtained by a user through the current search engine cannot meet their needs, they need to manually switch to one or more other search engines to search again and compare and filter the search results, which leads to cumbersome operation and low search efficiency.
A search method is provided that, in response to a user's search query on the current page, redirects the user to a new page and uses multiple search engines to respond to the same search term. The search results are then filtered based on the relevance of the search term to each search engine, and the target search result is automatically returned.
It eliminates the need for users to manually switch search engines, enabling automatic filtering, simplifying the operation process, and improving search efficiency.
Smart Images
Figure CN122196254A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of computer technology, and in particular to a search method, apparatus, device, storage medium, and computer program product. Background Technology
[0002] With the development of the Internet and the diversification of user needs, browsers have become the main tool for users to access the Internet. Users can search for the content they need through search engines in the browser's new tab to meet their own needs.
[0003] However, when the search results obtained by the current search engine cannot meet the user's needs, the user needs to manually switch to another or one more search engines to search again, and compare and filter the search results before selecting the desired results. This makes the entire search process cumbersome and inefficient.
[0004] The above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention
[0005] The main purpose of this application is to provide a search method, apparatus, device, storage medium, and computer program product, which aims to solve the technical problems of cumbersome operation and low search efficiency in the prior art.
[0006] To achieve the above objectives, this application provides a search method, the method comprising:
[0007] In response to a search command triggered by a user on the current page, the user is redirected to a new page, wherein the search command contains at least a search term;
[0008] The search terms are responded to by multiple search engines, and the target search results are displayed on the new page;
[0009] The target search result is obtained by filtering the search results of each search engine based on the relevance of the search term to each search engine.
[0010] In one embodiment, the step of responding to the search term through multiple search engines includes:
[0011] By responding to the search terms through multiple search engines, the search results corresponding to each of the search engines are obtained;
[0012] Determine the degree of relevance between the search terms and the category information of each of the search engines;
[0013] The search results corresponding to each of the aforementioned search engines are filtered according to the degree of relevance to obtain the target search results.
[0014] In one embodiment, the step of filtering the search results corresponding to each of the search engines based on the relevance to obtain the target search result includes:
[0015] The search results are sorted from highest to lowest relevance to obtain the sorting results;
[0016] From the sorting results, each search result is selected in descending order of relevance until the ranking of the selected candidate result is as low as the preset cutoff ranking.
[0017] Add the selected search results to the same list to obtain the target search results.
[0018] In one embodiment, the step of determining the relevance of the search term to the category information of each of the search engines includes:
[0019] The search terms are segmented to obtain multiple search terms;
[0020] Based on the semantic information of each segment, obtain the search related words corresponding to each segment;
[0021] Construct a search term library set, wherein the elements of the search term library set include each of the search terms and the corresponding search related terms;
[0022] The degree of relevance between the search terms and the category information of each of the search engines is determined based on the search term database.
[0023] In one embodiment, the step of determining the relevance of the search terms to the category information of each of the search engines based on the search term set includes:
[0024] Obtain a preset feature set associated with the category information of each of the search engines, wherein the elements in the preset feature set are feature words whose semantic relevance to the category information reaches a preset relevance;
[0025] Determine the intersection and union of elements between the search term set and each of the preset feature sets;
[0026] The relevance of the search terms to the category information of each search engine is determined by the intersection of the elements and the union of the corresponding elements.
[0027] In one embodiment, the step of determining the relevance of the search term to the category information of each search engine based on the intersection of the elements and the union of the corresponding elements includes:
[0028] Determine the number of intersection elements of the intersection of the aforementioned elements;
[0029] Determine the number of elements in the union of the unions of the aforementioned elements;
[0030] The ratio of the number of intersection elements to the number of union elements corresponding to each category of information;
[0031] Each ratio is used as a measure of the relevance between the search term and the information in each category.
[0032] In one embodiment, after the step of filtering the search results corresponding to each of the search engines according to the relevance to obtain the target search result, the method further includes:
[0033] Determine whether the relevance of each of the aforementioned search engines reaches the benchmark relevance level;
[0034] When the maximum relevance among the various relevance levels does not reach the benchmark relevance level, the target search engine corresponding to the maximum relevance level is determined;
[0035] Retrieve historical search results for the target obtained from previous responses to each element in the search term set;
[0036] The data source corresponding to the target search engine is updated based on the target's historical search results.
[0037] In one embodiment, before the step of determining the relevance of the search term to the category information of each of the search engines, the method further includes:
[0038] Determine the keywords for the search terms;
[0039] Obtain candidate web pages from each of the search results that match the keyword a preset number of times;
[0040] Extract the description tags of each candidate webpage;
[0041] The category information of the corresponding search engine is determined based on each of the described tags.
[0042] In one embodiment, the step of determining the category information of the corresponding search engine based on each of the description tags includes:
[0043] Obtain a preset category lexicon, which includes multiple preset category information;
[0044] Determine whether each of the description tags matches the preset category information in the preset category thesaurus;
[0045] When any description tag successfully matches any preset category information, the successfully matched preset category information is used as the category information of the search engine corresponding to the any description tag.
[0046] In one embodiment, after the step of determining whether each of the description tags matches each of the preset category information in the preset category thesaurus, the method further includes:
[0047] When any description tag does not match any of the preset category information, calculate the similarity between any description tag and each of the preset category information;
[0048] The preset category information with the highest similarity is selected as the category information of the search engine corresponding to any of the description tags.
[0049] In one embodiment, the step of calculating the similarity between any description tag and each of the preset category information when no description tag matches any of the preset category information includes:
[0050] When any description label does not match any of the preset category information, determine the label feature vector corresponding to any description label;
[0051] Obtain the category feature vectors corresponding to each of the preset category information;
[0052] The similarity between any descriptive label and each of the preset category information is calculated based on the label feature vector and each of the category feature vectors.
[0053] Furthermore, to achieve the above objectives, this application also proposes a search device, the device comprising:
[0054] The instruction response module is used to respond to a search instruction triggered by the user on the current page and jump to a new page, wherein the search instruction contains at least a search term;
[0055] The term search module is used to respond to the search terms through multiple search engines and display the target search results on the new page;
[0056] The target search result is obtained by filtering the search results of each search engine based on the relevance of the search term to each search engine.
[0057] In one embodiment, the term search module is further configured to respond to the search term through multiple search engines and obtain search results corresponding to each of the search engines;
[0058] The term search module is also used to determine the degree of relevance between the search term and the category information of each of the search engines;
[0059] The term search module is also used to filter the search results corresponding to each of the search engines according to the relevance to obtain the target search results.
[0060] In one embodiment, the term search module is further configured to sort the search results from high to low according to their relevance, and obtain a sorting result;
[0061] The term search module is also used to select each of the search results from the sorting results in descending order of relevance, until the ranking of the selected candidate result is as low as the preset cutoff ranking.
[0062] The term search module is also used to add the selected search results to the same list to obtain the target search results.
[0063] In one embodiment, the term search module is further configured to segment the search term to obtain multiple search terms;
[0064] The term search module is also used to obtain the search related terms corresponding to each term based on the semantic information of each term segment;
[0065] The term search module is also used to construct a search term library set, wherein the elements in the search term library set include each of the search terms and the corresponding search related terms;
[0066] The term search module is further configured to determine the degree of relevance between the search term and the category information of each of the search engines based on the search term database set.
[0067] In one embodiment, the term search module is further configured to obtain a preset feature set associated with the category information of each of the search engines, wherein the elements in the preset feature set are feature words whose semantic relevance to the category information reaches a preset relevance.
[0068] The term search module is also used to determine the intersection and union of elements of the search term set and each of the preset feature sets;
[0069] The term search module is further configured to determine the degree of relevance between the search term and the category information of each search engine based on the intersection of the elements and the union of the corresponding elements.
[0070] In one embodiment, the term search module is further configured to determine the keywords of the search term;
[0071] The term search module is also used to obtain candidate web pages in each of the search results that have matched the keyword a preset number of times;
[0072] The term search module is also used to extract description tags for each of the candidate web pages;
[0073] The term search module is also used to determine the category information of the corresponding search engine based on each of the description tags.
[0074] In addition, to achieve the above objectives, this application also proposes a search device, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the search method as described above.
[0075] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and which, when executed by a processor, implements the steps of the search method described above.
[0076] In addition, to achieve the above objectives, this application also proposes a computer program product comprising a computer program that, when executed by a processor, implements the steps of the search method described above.
[0077] One or more technical solutions proposed in this application have at least the following technical effects:
[0078] This application responds to a user's search command triggered on the current page, redirecting the user to a new page. The search command includes at least a search term. Multiple search engines respond to the search term, and the target search result is displayed on the new page. The target search result is obtained by filtering the search results from each search engine based on the relevance of the search term to each search engine. Because this application can respond to the same search term through multiple search engines and can filter the search results from each search engine based on the relevance of the search term to each search engine before providing the final target search result, compared to existing technologies, this application eliminates the need for users to manually switch search engines and also achieves automatic filtering, effectively simplifying user operations and improving search efficiency. Attached Figure Description
[0079] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.
[0080] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the 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.
[0081] Figure 1 This is a flowchart illustrating the first embodiment of the search method of this application;
[0082] Figure 2This is a flowchart illustrating the search process of the specified search engine in the first embodiment of this application;
[0083] Figure 3 This is a flowchart illustrating the second embodiment of the search method of this application;
[0084] Figure 4 This is a flowchart illustrating the third embodiment of the search method of this application;
[0085] Figure 5 This is a schematic diagram of the module structure of the search device of this application;
[0086] Figure 6 This is a schematic diagram of the structure of a search device suitable for implementing the embodiments of this application.
[0087] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation
[0088] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.
[0089] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.
[0090] The main solution of this application embodiment is: the search device responds to the search command triggered by the user on the current page and jumps to a new page, the search command containing at least a search term; multiple search engines respond to the search term and display the target search result on the new page; wherein, the target search result is obtained by filtering the search results of each search engine based on the relevance of the search term to each search engine.
[0091] In this embodiment, for ease of description, the following description will use the search device as the execution subject.
[0092] Because existing technology requires users to manually switch to one or more search engines to search again when the search results obtained by the current search engine cannot meet their needs, and to compare and filter the search results before selecting the desired results, the entire search process is cumbersome and inefficient.
[0093] This application provides a solution that allows multiple search engines to respond to the same search term, and can filter the search results of each search engine based on the relevance of the search term to each search engine before returning the final target search result. This eliminates the need for users to manually switch search engines and also enables automatic filtering, effectively simplifying user operations and improving search efficiency.
[0094] It should be noted that the executing entity in this embodiment can be a computing service device with content search, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device or search device capable of performing the above functions. The following description uses a search device as an example to illustrate this embodiment and the subsequent embodiments.
[0095] Based on this, embodiments of this application provide a search method, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the search method of this application.
[0096] In this embodiment, the search method includes steps S10 to S20:
[0097] Step S10: In response to a search command triggered by the user on the current page, redirect to a new page, wherein the search command contains at least a search term.
[0098] It should be noted that the above search terms can be keywords or phrases used to find relevant or required information.
[0099] In its implementation, the search device can display the browser's current page, such as a new tab. Users can enter search terms in the search bar of this page and click the corresponding search button, such as a search icon or the Enter key, triggering a search command containing the search terms. The search device can respond to this search command by redirecting to a new page, which then displays the search results for that search term. The search command can also include various search syntaxes or operators to allow the search engine to provide more accurate and efficient results in finding the desired information.
[0100] Step S20: Respond to the search term through multiple search engines and display the target search results on the new page.
[0101] The target search result is obtained by filtering the search results of each search engine based on the relevance of the search term to each search engine.
[0102] It should be noted that the aforementioned search engines are tools for searching information on the Internet and returning relevant results. Search engines can be integrated into the browser of a search device, serving as one of the main ways for users to find and access online resources.
[0103] In its implementation, after responding to a search command, the aforementioned search device can invoke the Application Programming Interface (API) provided by the browser client to retrieve multiple search engines from the network. If the API call fails or no search engine is successfully retrieved, it can retrieve a pre-built search engine from the current page. Once the search device has retrieved multiple search engines, it can use each search engine to respond to the search term. Each search engine can search for information matching the search term in its associated data source, including but not limited to various types of resources such as web pages, images, videos, news, and maps. Each search engine then sorts its found content to form its corresponding search results.
[0104] The aforementioned data sources can be collections of data used to support the functions and services of the search engine. Data sources can be indexed, enabling the search engine to quickly respond to search terms and provide relevant search results.
[0105] Furthermore, before displaying the final target search result, the aforementioned search device can determine the relevance of the search term to the search engine. Specifically, it can match the search engine's past search results with the current search term, assigning a relevance level to each search engine based on the number of times past search results matched the current search term. The higher the number of matches, the higher the relevance. The search device can then select the search results from search engines that achieve a pre-set relevance level and display these selected search results as the target search result on a new page.
[0106] This embodiment also supports users selecting a specific search engine for searching, see below. Figure 2 , Figure 2 This is a flowchart illustrating the search process of the specified search engine in the first embodiment of this application. Figure 2In this process, after the user opens the current page of the browser, the process begins. The aforementioned search device can determine whether the operation of calling the API interface provided by the browser client to obtain a list of search engines (including a list of multiple search engines) from the network was successful. If not, that is, the call to the API interface failed or the list of search engines was not successfully obtained, the device can obtain a list of search engines pre-built into the current page and then display the list of search engines in the drop-down box of the current page. The user can perform a selection operation on the list of search engines. The search device responds to the selection operation, determines the specified search engine selected by the user from the list of search engines, and then displays the specified search engine and its corresponding icon in the search bar of the current page. Then, it receives the search command containing search terms entered by the user, responds to the search terms using the specified search engine, returns the corresponding search results, and ends the process.
[0107] This embodiment responds to a user's search command triggered on the current page, redirecting the user to a new page. The search command includes at least a search term. Multiple search engines respond to the search term, and the target search result is displayed on the new page. The target search result is obtained by filtering the search results from each search engine based on the relevance of the search term to each search engine. Because this embodiment can respond to the same search term through multiple search engines and can filter the search results from each search engine based on the relevance of the search term to each search engine before providing the final target search result, compared to existing technologies, this embodiment eliminates the need for users to manually switch search engines and also achieves automatic filtering, effectively simplifying user operations and improving search efficiency.
[0108] Based on the first embodiment of this application, a second embodiment of this application is proposed. In the second embodiment, content that is the same as or similar to that in the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 3 , Figure 3 This is a flowchart illustrating the second embodiment of the search method of this application.
[0109] In this embodiment, the step of responding to the search term through multiple search engines may include: steps S201 to S203:
[0110] Step S201: The search terms are responded to by multiple search engines to obtain the search results corresponding to each search engine.
[0111] In practice, after the aforementioned search device obtains information from multiple search engines, it can use each search engine to respond to the search term. Each search engine can search for information matching the search term in its associated data source, and then sort the content found by each search engine to form its own corresponding search results.
[0112] Step S202: Determine the degree of relevance between the search terms and the category information of each search engine.
[0113] It should be noted that the category information mentioned above can refer to information representing the data type of the search results returned by the search engine, such as news categories, music categories, image categories, video categories, shopping categories, etc. The quality of search results from different search engines will vary depending on the category of the search term. For example, when the search term is related to shopping, search engines using the shopping category will produce search results that are more relevant to that search term.
[0114] In a specific implementation, the aforementioned search device can determine the category information of each search engine, then determine the semantic information of the search term currently entered by the user, and determine the degree of correlation between the semantic information and the category information.
[0115] In one feasible implementation, step S202 may include steps A21 to A24:
[0116] Step A21: Segment the search terms to obtain multiple search terms.
[0117] In practical implementation, the aforementioned search device can break down search terms into independent words or phrases to obtain multiple search terms, which are used to understand the composition and intent of the search terms.
[0118] Step A22: Obtain the search related words corresponding to each segment based on the semantic information of each segment.
[0119] In its implementation, the aforementioned search engine can obtain a pre-trained model built using natural language processing techniques, such as the BERT model, and use this pre-trained model to identify the semantic information of each word segment. Then, it can query search related words from a pre-built database that are consistent with the semantic information of each search word segment.
[0120] Step A23: Construct a search term library set, wherein the elements in the search term library set include each of the search terms and the corresponding search related terms.
[0121] In a specific implementation, the aforementioned search device can generate a blank set that does not contain any elements, and then add the aforementioned search terms and their corresponding search related terms as set elements to the blank set to form a search term library set.
[0122] Step A24: Determine the relevance of the search terms to the category information of each search engine based on the search term set.
[0123] In its implementation, the aforementioned search device can determine the matching of each element in the search term set with the category information of each search engine, and assign a corresponding relevance degree to different search engines based on the matching results. Among them, category information with a larger number of matching elements can be assigned a higher relevance degree.
[0124] In one feasible implementation, step A24 may include steps A241 to A243:
[0125] Step A241: Obtain a preset feature set associated with the category information of each search engine, wherein the elements in the preset feature set are feature words whose semantic relevance to the category information reaches a preset relevance.
[0126] It should be noted that the aforementioned preset relevance can be a pre-set relevance threshold used to determine whether the semantic relevance is high.
[0127] In practical implementation, for any category of information, feature words with a semantic relevance of a preset degree to that category of information can be retrieved from a pre-built database. These feature words are then added to the same set to form a preset feature set associated with that category of information. This process can be repeated to configure the associated preset feature set for each category of information. When constructing the search term library set corresponding to the user's currently input search term, the search device can obtain the preset feature set associated with each category of information.
[0128] Step A242: Determine the intersection and union of elements of the search term set and each of the preset feature sets.
[0129] In practical implementation, for any set of preset features associated with any category of information, the search device can determine the elements that coexist in the search term set and the preset feature set; these coexisting elements constitute the intersection of elements. Furthermore, the search device can also determine all the distinct elements in the search term set and the preset feature set; these distinct elements constitute the union of elements. Repeating the above process determines the intersection and union of elements for each preset feature set.
[0130] Step A243: Determine the relevance of the search term to the category information of each search engine based on the intersection of the elements and the union of the corresponding elements.
[0131] In a specific implementation, for any set of preset features associated with any category of information, the search device can determine the ratio of the occurrence of elements in the search term set in the preset feature set based on the intersection and union of elements. This ratio can be used as the degree of relevance between the search term and the category information. By repeating the search process, the degree of relevance between the search term and the category information of each search engine can be determined.
[0132] In one feasible implementation, step A243 may include steps A2431 to A2434:
[0133] A2431, determine the number of intersection elements of the intersection of the elements.
[0134] In a specific implementation, for any set of preset features corresponding to any category of information, the search device can count the number of intersection elements in the intersection of elements generated by the preset feature set.
[0135] A2433, determine the number of elements in the union of the union of the elements.
[0136] In a specific implementation, for any set of preset features corresponding to any category of information, the search device can count the number of elements in the union of the elements generated by the preset feature set.
[0137] A2433, based on the ratio of the number of intersection elements to the number of union elements corresponding to each category information.
[0138] In practical implementation, for any preset feature set, the search device can calculate the ratio of the number of intersection elements to the number of union elements, that is, the ratio of the number of elements in the search term set appearing in the preset feature set to the total number of elements in the preset feature set. By repeating the above process, the ratio of the number of intersection elements to the number of union elements corresponding to each category of information can be determined.
[0139] A2434, each ratio is used as the degree of relevance between the search term and each category of information.
[0140] In practice, the larger the ratio, the more frequently the elements in the search term set appear in the preset feature set corresponding to that ratio. In other words, the higher the relevance between the search term and the search engine corresponding to that ratio, the better. Based on this, the search device can use the obtained ratios as the degree of relevance between the search term and the corresponding category information.
[0141] Step S203: Filter the search results corresponding to each of the search engines according to the relevance to obtain the target search results.
[0142] In a specific implementation, the aforementioned search device can select search results corresponding to search engines whose relevance reaches a preset level, and display the selected search results as the target search results on a new page.
[0143] In one feasible implementation, step S203 may include steps S2031 to S2033:
[0144] Step S2031: Sort the search results from high to low according to their relevance to obtain a sorting result.
[0145] In practice, the aforementioned search device can, after determining the relevance between the search terms and the category information of each search engine, sort the search results of each search engine from high to low according to the relevance, and obtain the sorting result, that is, the search results with higher relevance will rank higher.
[0146] Step S2032: Select each search result from the sorting results in descending order of relevance until the ranking of the selected candidate result is as low as the preset cutoff ranking.
[0147] It should be noted that the aforementioned preset cutoff ranking can be a pre-defined term, and the relevance of the search results at the preset cutoff ranking is consistent with the pre-defined relevance of the search.
[0148] In a specific implementation, the aforementioned search device can select each search result sequentially from the sorted results in descending order of relevance, until the ranking of the selected candidate result is as low as the preset cutoff ranking.
[0149] Step S2033: Add the selected search results to the same list to obtain the target search results.
[0150] In its implementation, the aforementioned search device adds the selected search results to the same list to obtain the target search results. This achieves the goal of selecting search results from search engines with a pre-set relevance level, while also sorting the selected search results for easier viewing and improving the user experience.
[0151] In one feasible implementation, steps S204 to S207 may follow step S203:
[0152] Step S204: Determine whether the relevance of each search engine reaches the benchmark relevance level.
[0153] It should be noted that the aforementioned benchmark correlation level can be a pre-set threshold used to determine whether the correlation level is at a low level. That is, if any correlation level is lower than the benchmark correlation level, it can be determined that the correlation level is low and cannot meet the user's needs.
[0154] In practice, the aforementioned search device can compare the relevance of each search engine to the search term with the benchmark relevance to determine whether each relevance level reaches the benchmark relevance level.
[0155] Step S205: When the maximum relevance among the relevance levels does not reach the benchmark relevance level, determine the target search engine corresponding to the maximum relevance level.
[0156] In practice, when the search device detects that the maximum relevance among all relevance levels has not reached the benchmark relevance level, that is, when all relevance levels are lower than the benchmark relevance level, it determines that the fit between each search engine and the search term is not high and search results that meet the user's needs cannot be obtained. At this time, it is necessary to optimize the data source corresponding to each search engine. In order to improve the optimization efficiency, the target search engine corresponding to the maximum relevance level can be determined and the data source of the target search engine can be updated.
[0157] Step S206: Obtain the target historical search results obtained from each element in the search term set in the past response history.
[0158] In a specific implementation, the aforementioned search device can obtain target historical search results obtained from previous responses to any or all elements in the search term set, and these target historical search results can come from various search engines.
[0159] Step S207: Update the data source corresponding to the target search engine based on the target historical search results.
[0160] In practice, the aforementioned search device can update the target historical search results to the data source corresponding to the target search engine, thereby concentrating the search results of different search engines that have responded to the elements in the search term set into the target search engine with the highest relevance to the current search term, thus optimizing the target search engine. At the same time, the target historical search results can also be displayed in the sidebar of the new page.
[0161] This embodiment constructs a search term library set corresponding to search terms, determines the intersection and union of the search term library set and the preset feature sets corresponding to each list information, and improves the accuracy of determining the relevance by calculating the relevance between search terms and the category information of each search engine based on the number of intersection elements and the number of union elements. This indirectly improves the accuracy of filtering search results corresponding to each search engine based on the relevance, thereby improving search accuracy.
[0162] Based on the first and second embodiments of this application, a third embodiment of this application is proposed. In this third embodiment, content that is the same as or similar to the first and second embodiments described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 4 , Figure 4 This is a flowchart illustrating the third embodiment of the search method of this application.
[0163] In this embodiment, steps S21 to S24 may be included before step S202:
[0164] Step S21: Determine the keywords of the search term.
[0165] In practice, the aforementioned search device can break down search terms into independent words or phrases, and use the resulting search terms as keywords for the search terms.
[0166] Step S22: Obtain candidate web pages from each of the search results that have matched the keyword a preset number of times.
[0167] It should be noted that the above preset number of times can be a pre-set threshold representing a relatively large number of times.
[0168] In practice, the search results of each search engine can consist of several web pages. In order to avoid the low efficiency of the search engine in determining the category information due to the large number of web pages in the search results, the search device can select candidate web pages from each search result that have hit the keyword a preset number of times. If the number of times the keyword is hit in any search result does not reach the preset number of times, it can be determined that the search engine corresponding to the search result has a low correlation with the search term, and the search result generated by the search engine will not be used.
[0169] Step S23: Extract the description tags of each candidate webpage.
[0170] It should be noted that the above description tags can be tags that describe key information and content overview of candidate web pages.
[0171] In the implementation, each candidate webpage is displayed as a descriptive tag in the search results. Users can access the corresponding candidate webpage by clicking on the descriptive tag. The search device described above can extract the descriptive tags of the candidate webpages in each search result.
[0172] Step S24: Determine the category information of the corresponding search engine based on each of the description tags.
[0173] In a specific implementation, the search device can determine the category information of each candidate webpage based on the description tag, then count the number of times each category information appears, and take the category information with the most occurrences as the category information of the corresponding search engine.
[0174] In one feasible implementation, step S24 may include steps S241 to S243:
[0175] Step S241: Obtain a preset category lexicon, which includes multiple preset category information.
[0176] It should be noted that the above-mentioned preset category information can be pre-configured custom labels, which can be configured based on categories that appear frequently in actual scenarios.
[0177] In practical implementation, pre-configured custom category information can be used, and each pre-configured category information can be added to the same database to form a pre-configured category thesaurus. After determining the description tags of each candidate webpage, the search device can call upon this pre-configured category thesaurus.
[0178] Step S242: Determine whether each of the description tags matches the preset category information in the preset category thesaurus.
[0179] In its implementation, the aforementioned search device can traverse the preset category information in the preset category thesaurus based on each description tag to determine whether each description tag matches each preset category information.
[0180] Step S243: When any description tag successfully matches any preset category information, the successfully matched preset category information is used as the category information of the search engine corresponding to the any description tag.
[0181] In a specific implementation, when the search device detects that any description tag matches any preset category information in the preset category thesaurus, if the two match, the successfully matched preset category information can be used as the category information of the search engine that includes the description tag in the generated search results.
[0182] In any given search engine, when multiple description tags match different preset category information, the number of keywords that the candidate webpages corresponding to each description tag match in the search term can be determined. The description tag of the candidate webpage with the most matching keywords is then determined, and the category information matched by its description tag is used as the category information of the corresponding search engine.
[0183] In one possible implementation, steps S242 may be followed by steps S244 to S245:
[0184] Step S244: When any description tag does not match any of the preset category information, calculate the similarity between any description tag and each of the preset category information.
[0185] In a specific implementation, when any description tag generated by the search engine does not match any of the preset category information, the search device can calculate the similarity between the description tag and each preset category information, so as to determine the category information of the search engine from each preset category information through the similarity.
[0186] In one feasible implementation, step S244 may include steps S2441 to S2443:
[0187] Step S2441: When any description tag does not match any of the preset category information, determine the tag feature vector corresponding to any description tag.
[0188] In a specific implementation, when any description tag generated by the search engine does not match any of the preset category information, the search device can convert the description tag into a vector form through word embedding to obtain the tag feature vector corresponding to the description tag.
[0189] Step S2442: Obtain the category feature vector corresponding to each of the preset category information.
[0190] In a practical implementation, the preset category information can be pre-converted into category feature vectors in vector form using the same method described above, and then associated with each preset category information. After determining the tag feature vector of any descriptive label, the search device can obtain the category feature vectors associated with each preset category information.
[0191] Step S2443: Calculate the similarity between any descriptive label and each of the preset category information based on the label feature vector and each of the category feature vectors.
[0192] In its implementation, the search device can determine the lengths of the tag feature vector and the feature vectors of each category. For any preset category information, the search device can calculate the dot product between the tag feature vector and the feature vector of that category, and the product of the length of the tag feature vector and the length of the feature vector of that category. Dividing the dot product and the product yields the similarity between the tag feature vector and the feature vector of that category. This similarity is the similarity between the description tag and the preset category information. Repeating the above process yields the similarity between the description tag and each preset category information.
[0193] Step S245: Select the preset category information with the highest similarity as the category information of the search engine corresponding to any of the description tags.
[0194] In a specific implementation, for any description tag that does not match any of the preset category information, the search device can calculate the similarity between the description tag and each preset category information in the above manner, and then select the preset category information with the highest similarity as the category information of the search engine corresponding to that description tag.
[0195] This embodiment determines whether each description tag matches any preset category information in a preset category thesaurus. When any description tag matches any preset category information, the successfully matched preset category information is used as the category information of the search engine corresponding to any description tag. When no description tag matches any preset category information, the similarity between any description tag and each preset category information is calculated, and the preset category information with the highest similarity is selected as the category information of the search engine corresponding to any description tag, thereby accurately determining the category information of each search engine.
[0196] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the search method of this application. Any simple modifications based on this technical concept are within the protection scope of this application.
[0197] This application also provides a search device, please refer to... Figure 5 , Figure 5 This is a schematic diagram of the module structure of the search device of this application. The search device includes:
[0198] The instruction response module 10 is used to respond to a search instruction triggered by the user on the current page and jump to a new page, wherein the search instruction contains at least a search term;
[0199] The term search module 20 is used to respond to the search term through multiple search engines and display the target search results on the new page;
[0200] The target search result is obtained by filtering the search results of each search engine based on the relevance of the search term to each search engine.
[0201] The search device provided in this application, employing the search method described in the above embodiments, can solve the technical problems of cumbersome operation and low search efficiency. Compared with the prior art, the beneficial effects of the search device provided in this application are the same as those of the search method provided in the above embodiments, and other technical features in the search device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.
[0202] In one implementation, the term search module is further configured to respond to the search term through multiple search engines and obtain the search results corresponding to each of the search engines;
[0203] The term search module is also used to determine the degree of relevance between the search term and the category information of each of the search engines;
[0204] The term search module is also used to filter the search results corresponding to each of the search engines according to the relevance to obtain the target search results.
[0205] In one implementation, the term search module is further configured to sort the search results from high to low based on their relevance, thereby obtaining a sorting result;
[0206] The term search module is also used to select each of the search results from the sorting results in descending order of relevance, until the ranking of the selected candidate result is as low as the preset cutoff ranking.
[0207] The term search module is also used to add the selected search results to the same list to obtain the target search results.
[0208] In one implementation, the term search module is further used to segment the search term to obtain multiple search terms;
[0209] The term search module is also used to obtain the search related terms corresponding to each term based on the semantic information of each term segment;
[0210] The term search module is also used to construct a search term library set, wherein the elements in the search term library set include each of the search terms and the corresponding search related terms;
[0211] The term search module is further configured to determine the degree of relevance between the search term and the category information of each of the search engines based on the search term database set.
[0212] As one implementation, the term search module is further configured to obtain a preset feature set associated with the category information of each of the search engines, wherein the elements in the preset feature set are feature words whose semantic relevance to the category information reaches a preset relevance.
[0213] The term search module is also used to determine the intersection and union of elements of the search term set and each of the preset feature sets;
[0214] The term search module is further configured to determine the degree of relevance between the search term and the category information of each search engine based on the intersection of the elements and the union of the corresponding elements.
[0215] In one implementation, the term search module is further configured to determine the keywords of the search term;
[0216] The term search module is also used to obtain candidate web pages in each of the search results that have matched the keyword a preset number of times;
[0217] The term search module is also used to extract description tags for each of the candidate web pages;
[0218] The term search module is also used to determine the category information of the corresponding search engine based on each of the description tags.
[0219] This application provides a search device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform the search method in Embodiment 1 above.
[0220] The following is for reference. Figure 6 , Figure 6 This is a schematic diagram of the structure of a search device suitable for implementing the embodiments of this application. The search device in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 6 The search device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.
[0221] like Figure 6As shown, the search device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the search device. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. Communication device 1009 allows the search device to communicate wirelessly or wiredly with other devices to exchange data. Although the figure shows search devices with various systems, it should be understood that it is not required to implement or possess all of the systems shown. More or fewer systems may be implemented alternatively.
[0222] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.
[0223] The search device provided in this application, employing the search method described in the above embodiments, can solve the technical problems of cumbersome operation and low search efficiency. Compared with the prior art, the beneficial effects of the search device provided in this application are the same as those of the search method provided in the above embodiments, and other technical features of the search device are the same as those disclosed in the method of the previous embodiment, and will not be repeated here.
[0224] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.
[0225] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.
[0226] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to perform the search method in the above embodiments.
[0227] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.
[0228] The aforementioned computer-readable storage medium may be included in the search device or may exist independently without being assembled into the search device.
[0229] The aforementioned computer-readable storage medium carries one or more programs that, when executed by a search device, cause the search device to: respond to a search instruction triggered by a user on the current page, jump to a new page, wherein the search instruction contains at least a search term; respond to the search term through multiple search engines, and display the target search results on the new page.
[0230] Computer program code for performing the operations of this application can be written in one or more programming languages or a combination thereof, including object-oriented programming languages such as Java, Smalltalk, and C++, and conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).
[0231] 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 application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that 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 consecutively indicated 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, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.
[0232] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.
[0233] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described search method, thereby solving the technical problems of cumbersome operation and low search efficiency. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the search method provided in the above embodiments, and will not be repeated here.
[0234] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the search method described above.
[0235] The computer program product provided in this application can solve the technical problems of cumbersome operation and low search efficiency. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the search method provided in the above embodiments, and will not be repeated here.
[0236] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.
Claims
1. A search method, characterized in that, The method includes: In response to a search command triggered by a user on the current page, the user is redirected to a new page, wherein the search command contains at least a search term; The search terms are responded to by multiple search engines, and the target search results are displayed on the new page; The target search result is obtained by filtering the search results of each search engine based on the relevance of the search term to each search engine.
2. The search method as described in claim 1, characterized in that, The step of responding to the search term through multiple search engines includes: By responding to the search terms through multiple search engines, the search results corresponding to each of the search engines are obtained; Determine the degree of relevance between the search terms and the category information of each of the search engines; The search results corresponding to each of the aforementioned search engines are filtered according to the degree of relevance to obtain the target search results.
3. The search method as described in claim 2, characterized in that, The step of filtering the search results corresponding to each of the search engines according to the relevance to obtain the target search results includes: The search results are sorted from highest to lowest relevance to obtain the sorting results; The search results are selected sequentially from the sorting results in descending order of relevance until the ranking of the selected candidate results is as low as the preset cutoff ranking. Add the selected search results to the same list to obtain the target search results.
4. The search method as described in claim 2, characterized in that, The step of determining the relevance of the search terms to the category information of each of the search engines includes: The search terms are segmented to obtain multiple search terms; Based on the semantic information of each segment, obtain the search related words corresponding to each segment; Construct a search term library set, wherein the elements of the search term library set include each of the search terms and the corresponding search related terms; The degree of relevance between the search terms and the category information of each of the search engines is determined based on the search term database.
5. The search method according to any one of claims 2 to 4, characterized in that, Before the step of determining the relevance of the search terms to the category information of each of the search engines, the method further includes: Determine the keywords for the search terms; Obtain candidate web pages from each of the search results that match the keyword a preset number of times; Extract the description tags of each candidate webpage; The category information of the corresponding search engine is determined based on each of the described tags.
6. The search method as described in claim 5, characterized in that, The step of determining the corresponding search engine category information based on each of the description tags includes: Obtain a preset category lexicon, which includes multiple preset category information; Determine whether each of the description tags matches the preset category information in the preset category thesaurus; When any description tag successfully matches any preset category information, the successfully matched preset category information is used as the category information of the search engine corresponding to the any description tag.
7. A search device, characterized in that, The device includes: The instruction response module is used to respond to a search instruction triggered by the user on the current page and jump to a new page, wherein the search instruction contains at least a search term; The term search module is used to respond to the search terms through multiple search engines and display the target search results on the new page; The target search result is obtained by filtering the search results of each search engine based on the relevance of the search term to each search engine.
8. A search device, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the search method as described in any one of claims 1 to 6.
9. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the search method as described in any one of claims 1 to 6.
10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps of the search method as described in any one of claims 1 to 6.