Information searching method and device

Abstract

The invention discloses an information search method and device, relates to the technical field of data query, and mainly aims to improve the information search efficiency of a user. According to the main technical scheme, information search parameters customized by a user in a search configuration area are obtained, wherein the information search parameters at least comprise target category labels corresponding to category screening items of dynamic update categories and target keywords corresponding to keyword input items of dynamic recommendation vocabularies; generating an information search request based on the target category label and the target keyword, and sending the information search request to a database, so that the database executes a corresponding search operation based on the information search request, and obtains an information search result; and displaying the information search results in the result display area according to a preset display rule, wherein the preset display rule is used for representing a display rule for unfolding or folding each result in the information search results according to a preset number. The method and the device are used for information search.

Description

Technical Field

[0001] The present application relates to the field of data query technology, and in particular to an information search method and device. Background Art

[0002] In the design of enterprise platform business software, the information search function is an integral part of the user interface, which is designed to help users efficiently find the information they need. In order to improve the efficiency of information retrieval, the information search function often needs to provide specific filter items and keyword searches based on the fields in the database.

[0003] At present, the traditional approach is to lay out filter items at the top of the page, and users select them according to their search needs, and the corresponding search results are displayed on the page. Although this method is intuitive, it has obvious defects. When there are too many filter items, it will occupy a large area of ​​the interface. Even if the "expand" and "collapse" methods are set to reduce visual confusion due to the size of the computer screen, users still need to switch and select frequently when operating, which affects the efficiency of users' information search and thus reduces the user experience. Summary of the invention

[0004] In view of the above problems, the present application provides an information search method and device, the main purpose of which is to improve the user's information search efficiency.

[0005] In order to solve the above technical problems, this application proposes the following solutions:

[0006] In a first aspect, the present application provides an information search method, the method comprising:

[0007] Acquire information search parameters customized by the user in the search configuration area, wherein the information search parameters at least include a target category label corresponding to a category filter item of a dynamically updated category and a target keyword corresponding to a keyword input item of a dynamically recommended vocabulary;

[0008] generating an information search request based on the target category tag and the target keyword, and sending the information search request to a database, so that the database performs a corresponding search operation based on the information search request and obtains information search results;

[0009] The information search results are displayed in a result display area according to a preset display rule, wherein the preset display rule is used to represent a display rule for expanding or collapsing each result in the information search results according to a preset number.

[0010] In a second aspect, the present application provides an information search device, the device comprising:

[0011] An acquisition unit, used to acquire information search parameters customized by the user in the search configuration area, wherein the information search parameters at least include a target category label corresponding to a category filter item of a dynamically updated category and a target keyword corresponding to a keyword input item of a dynamically recommended vocabulary;

[0012] A first processing unit, configured to generate an information search request based on the target category label and the target keyword obtained by the acquisition unit, and send the information search request to a database, so that the database performs a corresponding search operation based on the information search request and obtains information search results;

[0013] A display unit is used to display the information search results obtained by the first processing unit in a result display area according to a preset display rule, wherein the preset display rule is used to represent a display rule for expanding or collapsing each result in the information search results according to a preset number.

[0014] In order to achieve the above-mentioned purpose, according to the third aspect of the present application, a storage medium is provided, wherein the storage medium includes a stored program, wherein when the program is running, the device where the storage medium is located is controlled to execute the information search method of the above-mentioned first aspect.

[0015] In order to achieve the above-mentioned purpose, according to a fourth aspect of the present application, a processor is provided, wherein the processor is used to run a program, wherein the information search method of the above-mentioned first aspect is executed when the program is running.

[0016] By means of the above technical scheme, the present application provides an information search method and device, which is to first obtain the information search parameters customized by the user in the search configuration area when it is necessary to search for information, and the information search parameters at least include the target category label corresponding to the category filter item of the dynamically updated category and the target keyword corresponding to the keyword input item of the dynamically recommended vocabulary, then generate an information search request based on the target category label and the target keyword, and send the information search request to the database, so that the database performs the corresponding search operation based on the information search request and obtains the information search results, and finally, displays the information search results in the result display area according to the preset display rules, and the preset display rules are used to represent the display rules for expanding or collapsing each result in the information search results according to the preset number. Through the technical scheme provided by the present application, the corresponding information search parameters can be quickly and accurately configured by using the category filter items of the dynamically updated category and the keyword input items of the dynamically recommended vocabulary, and after the information search parameters are determined, they can be automatically converted into information search requests, and each result in the obtained information search results can be expanded or collapsed according to the preset number in the result display area. Equivalent to the existing technology, the automatic update of filter item categories and the automatic recommendation of keywords when configuring information search parameters not only eliminate the need for users to frequently switch and select, greatly reducing the operating burden, but also effectively improving the user's search efficiency for information, and automatically updating categories with a preset number of results displayed, which facilitates users to view intuitively while optimizing the interface layout, providing a better user experience.

[0017] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present application. Also, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0019] Figure 1 A flow chart of an information search method provided by an embodiment of the present application is shown;

[0020] Figure 2 Another information search method flow chart provided by an embodiment of the present application is shown;

[0021] Figure 3 A block diagram showing a composition of an information search device provided by an embodiment of the present application is shown;

[0022] Figure 4 A block diagram showing the composition of another information search device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0023] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0024] At present, the traditional approach is to lay out the filter items at the top of the page and let users select them according to their needs. Although this approach is intuitive, it has obvious flaws. When there are too many filter items, a large amount of interface space will be occupied. Even if the "expand" and "collapse" methods are set to reduce visual confusion due to the size of the computer screen, users still need to switch and select frequently during operation, which affects the efficiency of users' information search and thus reduces the user experience.

[0025] After research, the inventor found that a filter item that can dynamically update categories in one area of ​​the interface and a keyword input item that can automatically recommend words can be set as an information search area. The corresponding information search parameters can be quickly and accurately configured through the filter item and input item. After the information search parameters are determined, they can be automatically converted into information search requests, and each result in the obtained information search results can be expanded or folded and displayed according to a preset number in another area of ​​the interface. In this way, there is no need for users to frequently switch and select, which reduces the operating burden. It not only effectively improves the efficiency of users' information search, but also automatically updates categories with a preset number of results, which is convenient for users to intuitively view and optimizes the interface layout, providing a better user experience.

[0026] Based on the above considerations, the embodiment of the present application provides an information search method, which can improve the efficiency of users' information search. The execution subject of the embodiment of the present application is an electronic device equipped with a search device, such as a browser, software including a search function, etc. In some scenarios, the execution subject of the present application is an internal business management system of an enterprise, including but not limited to a human resources management system, a customer relationship management system, or a financial management system, etc. The specific execution steps are as follows: Figure 1 As shown, at least 101-103 are included.

[0027] 101. Obtain information search parameters customized by the user in the search configuration area.

[0028] In this embodiment, the information search parameters at least include the target category label corresponding to the category filter item of the dynamically updated category and the target keyword corresponding to the keyword input item of the dynamically recommended vocabulary. Among them, the search configuration area is a parameter configuration area pre-deployed at the top or side of the front-end search interface, and the corresponding category filter items and keyword input items are set in the search configuration area according to the fields in the database. The category filter item can be customized to set the default filter items of a specified number of categories according to the size of the search configuration area or user needs. On this basis, the category corresponding to the default filter item can be automatically updated according to the user behavior data within a period of time or the user-defined update logic. It can be set according to different time periods or different users. This embodiment does not limit this. The keyword input item can be used alone to realize fuzzy search, or it can be used in combination with the category filter item to realize accurate search. Therefore, in order to improve the efficiency of keyword input, related associative keywords can be automatically recommended before the target keyword is entered in the keyword input item. Specifically, the display can be switched in the input item box. The associative keywords are recommended in combination with the high-frequency fields of the database, such as relatively high-frequency ones in all fields, or relatively high-frequency ones in the fields corresponding to the user's target category label, which is not limited in this embodiment. After the user selects the target category label and the target keyword, the user-defined information search parameter is obtained.

[0029] 102. Generate an information search request based on the target category tag and the target keyword, and send the information search request to a database, so that the database performs a corresponding search operation based on the information search request and obtains information search results.

[0030] Since the user has completed the selection of the target category tag and the input of the target keyword, an information search request can be generated based on the selection of the target category tag and the target keyword. The request contains the category tag selected by the user and the keyword input. The request is sent to the corresponding database. After receiving the request, the database will parse the category tag and keyword therein and perform the corresponding search operation. That is, the database screens out the information items that meet the requirements by matching the category tag selected by the user and the keyword input, generates an information search result, and after obtaining the information search result, the information search result is fed back to the front-end search interface to execute step 103.

[0031] 103. Display the information search results in the result display area according to the preset display rules.

[0032] In this embodiment, the preset display rule is used to characterize the display rule for expanding or collapsing each result in the information search results according to a preset number. Among them, the result display area is a result display area pre-deployed in the middle or bottom of the front-end search interface. After the information search results fed back by the database are obtained on the front-end search interface, the information search results can be displayed in the result display area according to the preset display rules. The preset display rule is to control the presentation mode of the information search results, such as determining which results in the information search results are expanded by default and which results are collapsed by default. Specifically, it can be analyzed based on user behavior data to determine user preferences, search history, relevance of results or other factors combined with the display quantity limit to set. Taking relevance as an example, the relevance of each result is sorted by quality score and the results with low relevance are collapsed by default. At this time, the system can sort first, then mark the high relevance as expanded, and mark other low relevance results as collapsed after meeting the display quantity limit. At the same time, the amount of content for each result in the information search results may be different. Therefore, in order to further improve the display effect so that users can quickly know the relevant information of each result, a content summary of each result can be generated, such as setting the number of words, paragraphs, information density and other restrictions to make the summary content as concise as possible. In addition, since some results in the information search results may be folded by default because they are marked as collapsed, in order to improve the convenience for users to view these results, these folded results can be expanded by clicking or sliding to view the corresponding details.

[0033] Based on the above Figure 1It can be seen from the implementation method that the information search method provided by the present application is that when it is necessary to search for information, first, the information search parameters customized by the user in the search configuration area are obtained, and the information search parameters at least include the target category label corresponding to the category filter item of the dynamically updated category and the target keyword corresponding to the keyword input item of the dynamically recommended vocabulary. Then, an information search request is generated based on the target category label and the target keyword, and the information search request is sent to the database, so that the database performs the corresponding search operation based on the information search request and obtains the information search results. Finally, the information search results are displayed in the result display area according to the preset display rules, and the preset display rules are used to represent the display rules for expanding or collapsing each result in the information search results according to the preset number. Through the technical solution provided by the present application, the corresponding information search parameters can be quickly and accurately configured using the category filter items of the dynamically updated category and the keyword input items of the dynamically recommended vocabulary. After the information search parameters are determined, they can be automatically converted into information search requests, and each result in the obtained information search results can be expanded or collapsed according to the preset number in the result display area. Equivalent to the existing technology, the automatic update of filter item categories and the automatic recommendation of keywords when configuring information search parameters not only eliminate the need for users to frequently switch and select, greatly reducing the operating burden, but also effectively improving the user's search efficiency for information, and automatically updating categories with a preset number of results displayed, which facilitates users to view intuitively while optimizing the interface layout, providing a better user experience.

[0034] Further, the preferred embodiment of the present application is in the above Figure 1 Based on this, a detailed description of the information search process is given, with specific steps as follows Figure 2 As shown, including at least 201-209.

[0035] 201. Logically classify the database fields according to the preset classification system to obtain category labels, and automatically generate category filter items based on the category labels.

[0036] In this step, the preset classification system is used to characterize the classification framework under different business scenarios. In practical applications, there are many different business scenarios within the enterprise, and different business scenarios have different business needs and data structures, which constitute the business scenario framework. Therefore, a clear classification system can be pre-defined according to business needs and data structures. The system should cover all important field categories under the corresponding business scenario and be logically independent of each other. For example, in a human resource management system, it may be necessary to screen talents according to educational background, work experience, skills, working language, etc. Then the fields can be divided into multiple levels such as personal information, educational experience, work experience, skill certificates, working language, etc. as category labels, and each field in the database under the business field is mapped to the corresponding category label. For example, "graduation school" and "graduation major" can be classified into the "education experience" category. According to the obtained category label, the system automatically creates corresponding analogy filter items. These category filter items can be set to expand one part and collapse the other part according to the needs of the user. The expanded category filter items can be presented in the form of drop-down menus, check boxes, etc., for user selection. For example, if you create a category filter item for "Educational Experience", users can select "Bachelor's Degree", "Master's Degree", or "PhD".

[0037] Since in this embodiment, the category filter items are automatically updated, so in order to make the updated categories more in line with the user's search needs, thereby improving the overall efficiency of information retrieval. The specific execution process of automatically generating category filter items according to category tags is: obtaining user behavior data for different time periods; determining the preference category tags corresponding to different time periods and the arrangement order corresponding to the preference category tags in the category tags based on the user behavior data for different time periods, and the arrangement order is used to characterize the preference strength of the preference category tags; automatically generating category filter items for different time periods according to the preference category tags and the arrangement order.

[0038] Among them, different time periods can be based on the working hours of the enterprise and can be customized according to time units such as morning, afternoon, and 2 hours. By obtaining user behavior data of different time periods in the past through log files, database records or real-time monitoring, the behavior data includes but is not limited to information such as user clicks, searches, browsing history, stay time, and interaction frequency, it is possible to identify the user's preference trends in different time periods. Based on the preference trend, the user's preferred category label can be determined in the category label, that is, the preferred category label is obtained. However, since there may be multiple preference category labels in the same time period, in order to enable the corresponding category filter items to be sorted according to the priority of preference, the preference strength of different preference category labels can be analyzed at this time, such as by calculating the frequency of being accessed or searched. According to the sorted preference category labels, the system automatically creates corresponding category filter items, which can be displayed to the user in the order of preference strength, so that the user can quickly locate and select. It should be noted that since the preference category labels corresponding to different time periods are determined, the category filter items corresponding to different time periods can be automatically updated and displayed as time changes. This allows users to more conveniently select target category labels that meet search requirements in the category filter items after the updated categories in real time, thereby improving the overall efficiency of information retrieval.

[0039] 202. Determine a recommended field in the database field using a preset recommendation algorithm, and automatically add the recommended field as an associative keyword in the keyword input item.

[0040] In this step, the preset recommendation algorithm can be selected or developed based on quantitative indicators such as the frequency of use, relevance, and importance of the fields in the database. Through the recommendation algorithm, the fields in the database are deeply analyzed to identify which fields are highly used, highly relevant, or highly valuable. Since these fields are more likely to be selected, these fields can be used as recommended fields. At the same time, the recommendation function is integrated into the keyword input box of the front-end search interface. Before the user starts typing, the recommended field is automatically switched in the keyword input box as an associative keyword, such as each associative keyword is displayed for 5 seconds. In order to further match the target category label selected by the user, the field corresponding to the target category label can be circled for analysis when determining the recommended field to increase the relevance of the associative keyword with the target category target.

[0041] In order to further increase the relevance of the associated keywords and the target category targets and improve the overall efficiency of information search, the specific execution process of determining the recommended fields from the database fields using the preset recommendation algorithm is as follows: detect whether there is a selected category label in the category filter item; if so, extract the high-frequency fields in the field corresponding to the selected category label as the recommended fields; if not, extract the high-frequency fields in the database fields as the recommended fields.

[0042] Among them, the selected category label refers to the target category label selected by the user in the category filter item. If it is detected that the user has selected the target category label, the field set directly associated with the category label is queried in the database, and the high-frequency fields are extracted from the field set as recommended fields. If it is not detected that the user has selected the target category label, a global statistics is performed in all fields in the database to find the global high-frequency fields as high-frequency fields. In addition, the extracted high-frequency fields can also be organized into a recommended field list to facilitate subsequent automatic filling or associative keyword display based on the recommended field list. It should be noted that the high-frequency fields in this embodiment can be determined by setting corresponding thresholds through expert systems or domain knowledge and by direct numerical comparison, or can be determined by cluster analysis or machine learning models. This embodiment does not limit this.

[0043] 203. Obtain information search parameters customized by the user in the search configuration area.

[0044] This step is combined with the description of step 101 in the above method, and the same contents will not be repeated here.

[0045] 204. Determine whether the target keyword and the target category label meet the relevance requirements.

[0046] In this step, the relevance requirement is used to characterize the semantic similarity requirement between the keyword and the category label. Specifically, a pre-trained word vector model (such as Word2Vec, G loVe or BERT, etc.) or a self-built word vector model can be used to convert the target keyword and the target category label into a vector representation. Specifically, the cosine similarity, Euclidean distance, Jaccard similarity coefficient and other methods can be used to calculate the similarity between the target keyword vector and the corresponding field vector of the target category label. At the same time, a semantic similarity threshold is set according to business needs and domain knowledge. The semantic similarity threshold determines the minimum relevance requirement between the target keyword and the target category label. By comparing the two, it can be determined whether the target keyword is related to the target category label. If so, step 205 is executed, otherwise step 206 is executed.

[0047] In order to ensure the accuracy and rationality of the correlation determination, the specific implementation process of the above-mentioned determination of whether the target keyword and the target category label meet the correlation requirements is: determining whether the target keyword and the target category label meet the correlation requirements, including: calculating the semantic similarity of the target keyword and the target category label corresponding fields; determining one by one whether the semantic similarities are all less than the similarity threshold; if not, the target keyword and the target category label meet the correlation requirements; if so, the target keyword and the target category label do not meet the correlation requirements.

[0048] Since the target category label may correspond to multiple fields, the semantic similarity between the target keyword and the field corresponding to the target category label can be calculated through the above-mentioned word vector model, and the obtained semantic similarity can be compared with the similarity threshold one by one. If all semantic similarities are less than the similarity threshold, it means that the target keyword and the fields corresponding to the target category label are not similar. At this time, it can be considered that the target keyword and the target category label do not meet the relevance requirements. On the contrary, if there is one or more semantic similarities greater than or equal to the similarity threshold, it means that some fields in the fields corresponding to the target keyword and the target category label have a similar relationship. At this time, it can be considered that the target keyword and the target category label meet the relevance requirements.

[0049] 205. Generate an information search request based on the target category tag and the target keyword, and send the information search request to a database, so that the database performs a corresponding search operation based on the information search request and obtains information search results.

[0050] This step is combined with the description of step 102 in the above method, and the same contents are not repeated here.

[0051] 206. Generate prompt information for changing keywords or category labels.

[0052] Since it has been determined in step 204 that the target keyword and the target category tag are not related before, if a hasty search is performed at this time, the accuracy of the search results obtained will be affected. Therefore, in order to ensure the rationality of the configuration of the information search parameters, so that the information search results obtained meet the user's search needs, a prompt message for keyword replacement or category tag replacement can be generated at this time to prompt the user to replace the keyword or category tag. The prompt message may include a replacement direction generated based on the keyword or category tag that needs to be replaced, as well as specific replacement keywords or replacement category tags. Specifically, the replacement priority, that is, the replacement direction, can be determined based on whether the screening operation corresponding to the category screening item and the input operation corresponding to the keyword input box are monitored. At the same time, for replacing the category tag, the semantic similarity of the target keyword and the field corresponding to other category tags in the category screening item except the target category tag can be calculated respectively, and sorting and filtering can be performed according to the semantic similarity to select other category tags with higher rankings as replacement category tags. For replacing keywords, the associative keywords obtained from the high-frequency fields corresponding to the target category tags in the above steps can be used as replacement keywords as specific replacement keywords or replacement category tags. By providing change directions and change prompt words, it can effectively help users improve their searches, thereby making it easier for users to adjust current information search parameters in a targeted manner.

[0053] 207. Perform quality assessment and summary generation on each result in the information search results to obtain a quality score and content summary corresponding to each result in the information search results.

[0054] In this step, the quality assessment can be comprehensively considered from multiple dimensions, including but not limited to the relevance, authority, novelty and completeness of the information. Specifically, a machine learning model can be trained to analyze the degree of match between the document content and the query to reflect the relevance, the number of citations to reflect the authority, the publication date to judge the novelty, and the document length or the number of paragraphs to measure the completeness. Set a corresponding weight for each dimension to determine the quality score corresponding to each result in the information search results. The summary generation can be such as extractive summary (Extractive Summarization) or abstract summary (Abstract Summarization) and other summary generation techniques, automatically extract key sentences from each result or generate a concise overview, that is, a content summary, so as to help users quickly understand the main content of the document. In order to further fit the size of the result display area, corresponding restrictions such as the number of words, paragraphs, and information density can also be set to make the summary content as concise as possible.

[0055] 208. Determine results to be displayed and results to be hidden in the information search results according to a preset display quantity and quality score.

[0056] In this step, the preset display quantity refers to the number of search results displayed by default determined according to the user interface design and user experience principles, such as the first 5 or the first 10. Since the quality score of each result has been determined in step 207, the results can be sorted according to the quality score at this time, and the results ranked within the preset display quantity are selected as the results to be displayed and marked as expanded, and the remaining results are used as the results to be hidden and marked as collapsed.

[0057] 209. Expand and display the quality scores and content summaries corresponding to the results to be displayed in the first partition of the result display area, and fold and display the quality scores and content summaries corresponding to the results to be hidden in the second partition of the result display area.

[0058] In this step, the first partition is used to display the content summary and quality score of the results to be displayed. In addition, the complete title, source link, etc. can also be attached, while the second partition is used to fold and display the content summary and quality score of the results to be hidden. Since the second partition is also likely to be viewed, a "view more" or "expand more results" button can be provided in the second partition. After the user clicks it, the results to be folded can be expanded for easy viewing. In order to avoid loading too much content at one time to affect the user experience, the second partition can be paging or scrolling to gradually display the results to be hidden. The size and position of the first partition and the second partition can be customized according to the needs of the user, and this embodiment does not limit this.

[0059] Furthermore, as a response to the above Figure 1-2 The implementation of the method embodiment shown in the figure, the embodiment of the present application provides an information search device, which is used to improve the efficiency of the user's search for information. The embodiment of the device corresponds to the aforementioned method embodiment. For the sake of ease of reading, this embodiment will no longer repeat the details of the aforementioned method embodiment one by one, but it should be clear that the device in this embodiment can correspond to all the contents of the aforementioned method embodiment. Specifically, Figure 3 As shown, the device comprises:

[0060] An acquisition unit 31 is used to acquire information search parameters customized by the user in the search configuration area, wherein the information search parameters at least include a target category label corresponding to a category filter item of a dynamically updated category and a target keyword corresponding to a keyword input item of a dynamically recommended vocabulary;

[0061] A first processing unit 32 is used to generate an information search request based on the target category label and the target keyword obtained by the acquisition unit 31, and send the information search request to a database, so that the database performs a corresponding search operation based on the information search request and obtains information search results;

[0062] The display unit 33 is used to display the information search results obtained by the first processing unit 32 in the result display area according to a preset display rule, and the preset display rule is used to represent the display rule for expanding or collapsing each result in the information search result according to a preset number.

[0063] Further, such as Figure 4 As shown, the device also includes:

[0064] A second processing unit 34 is used to logically classify the database fields according to a preset classification system before the acquisition unit 31 to obtain category labels, and automatically generate the category filter items according to the category labels, wherein the preset classification system is used to represent a category classification framework in different business scenarios;

[0065] The third processing unit 35 is used to determine a recommended field in the database field by using a preset recommendation algorithm, and automatically add the recommended field as an associative keyword in the keyword input item.

[0066] Further, such as Figure 4 As shown, the second processing unit 34 includes:

[0067] An acquisition module 341 is used to acquire user behavior data in different time periods;

[0068] A first processing module 342 is used to determine the preference category labels corresponding to different time periods and the arrangement order corresponding to the preference category labels in the category labels based on the user behavior data of different time periods obtained by the acquisition module 341, and the arrangement order is used to represent the preference strength of the preference category labels;

[0069] The generating module 343 is used to automatically generate the category filtering items of different time periods according to the preference category labels and the arrangement order obtained by the first processing module 342 .

[0070] Further, such as Figure 4 As shown, the third processing unit 35 includes:

[0071] A detection module 351 is used to detect whether there is a selected category label in the category filter item;

[0072] An extraction module 352 is configured to extract a high-frequency field in a field corresponding to the selected category label as the recommended field if the detection module 351 detects that there is a selected category label in the category filter item;

[0073] The extraction module 352 is further configured to extract high-frequency fields in the database fields as the recommended fields if the detection module 351 detects that there is no selected category label in the category filter item.

[0074] Further, such as Figure 4 As shown, the device also includes:

[0075] A judging unit 36, configured to judge whether the target keyword and the target category label meet the relevance requirement before the first processing unit 32;

[0076] The first processing unit 32 is specifically configured to:

[0077] If the determination unit 36 ​​determines that the target keyword and the target category tag meet the relevance requirement, an information search request is generated based on the target category tag and the target keyword, and the information search request is sent to the database, so that the database performs a corresponding search operation based on the information search request and obtains information search results;

[0078] The prompt unit 37 is used to generate a prompt message for changing a keyword or a category label if the judging unit 36 ​​judges that the target keyword and the target category label do not meet the relevance requirement.

[0079] Further, such as Figure 4 As shown, the judging unit 36 ​​includes:

[0080] A calculation module 361 is used to calculate the semantic similarity between the target keyword and the field corresponding to the target category label;

[0081] A judging module 362, used for judging one by one whether the semantic similarities obtained by the calculating module 361 are all less than a similarity threshold;

[0082] A first determination module 363, configured to determine that if the determination module 362 determines that the semantic similarities are not all less than a similarity threshold, then the target keyword and the target category label meet the relevance requirement;

[0083] The determination module 363 is further configured to determine that if the judgment module 362 judges that the semantic similarities are all less than a similarity threshold one by one, then the target keyword and the target category label do not meet the relevance requirement.

[0084] Further, such as Figure 4 As shown, the display unit 33 includes:

[0085] The second processing module 331 is used to perform quality assessment and summary generation on each result in the information search results, and obtain a quality score and content summary corresponding to each result in the information search results;

[0086] A second determination module 332, configured to determine results to be displayed and results to be hidden in the information search results according to a preset display quantity and the quality score obtained by the second processing module 331;

[0087] The display module 333 is used to expand and display the quality score corresponding to the to-be-displayed result obtained by the second determination module 332 and the content summary obtained by the second processing module 331 in the first partition of the result display area, and to fold and display the quality score corresponding to the to-be-hidden result obtained by the second determination module 332 and the content summary obtained by the second processing module 331 in the second partition of the result display area.

[0088] Furthermore, an embodiment of the present application further provides a storage medium, wherein the storage medium is used to store a computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute the above Figure 1-2 The information search method described in .

[0089] Furthermore, the embodiment of the present application also provides a processor, the processor is used to run a program, wherein the program executes the above Figure 1-2 The information search method described in .

[0090] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0091] It is understandable that the related features in the above methods and devices can be referenced to each other. In addition, the "first", "second" and the like in the above embodiments are used to distinguish the embodiments, but do not represent the advantages and disadvantages of the embodiments.

[0092] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0093] The algorithm and display provided herein are not inherently related to any particular computer, virtual system or other device. Various general purpose systems can also be used together with the teachings based on this. According to the above description, it is obvious that the structure required for constructing such systems. In addition, the application is not directed to any specific programming language either. It should be understood that various programming languages ​​can be utilized to realize the content of the application described herein, and the description of the specific language above is for the purpose of disclosing the best mode of implementation of the application.

[0094] In addition, the memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0095] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0096] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0097] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0098] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0099] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0100] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0101] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include transitory media such as modulated data signals and carrier waves.

[0102] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0103] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0104] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. An information search method, characterized in that: The method comprises: Acquire information search parameters customized by the user in the search configuration area, wherein the information search parameters at least include a target category label corresponding to a category filter item of a dynamically updated category and a target keyword corresponding to a keyword input item of a dynamically recommended vocabulary; generating an information search request based on the target category tag and the target keyword, and sending the information search request to a database, so that the database performs a corresponding search operation based on the information search request and obtains information search results; The information search results are displayed in a result display area according to a preset display rule, wherein the preset display rule is used to represent a display rule for expanding or collapsing each result in the information search results according to a preset number.

2. The method according to claim 1, characterized in that Before obtaining the information search parameters customized by the user in the search configuration area, the method further includes: Logically classify the database fields according to a preset classification system to obtain category labels, and automatically generate the category filter items according to the category labels, wherein the preset classification system is used to represent a category classification framework in different business scenarios; A preset recommendation algorithm is used to determine a recommended field in the database field, and the recommended field is automatically added as an associative keyword in the keyword input item.

3. The method according to claim 2, characterized in that Automatically generating the category filter items according to the category labels includes: Obtain user behavior data at different time periods; Determine, in the category tags, preference category tags corresponding to different time periods and an arrangement order corresponding to the preference category tags based on the user behavior data of different time periods, wherein the arrangement order is used to characterize the preference strength of the preference category tags; The category filter items for different time periods are automatically generated according to the preference category labels and the arrangement order.

4. The method according to claim 2, characterized in that: Determining a recommended field from the database field using a preset recommendation algorithm includes: Detect whether there is a selected category label in the category filter item; If yes, extract the high-frequency fields in the fields corresponding to the selected category labels as the recommended fields; If not, then extract the high-frequency fields in the database fields as the recommended fields.

5. The method according to claim 1, characterized in that: Before generating an information search request based on the target category tag and the target keyword and sending the information search request to a database so that the database performs a corresponding search operation based on the information search request and obtains information search results, the method further includes: Determine whether the target keyword and the target category label meet the relevance requirement; If so, an information search request is generated based on the target category tag and the target keyword, and the information search request is sent to a database, so that the database performs a corresponding search operation based on the information search request and obtains information search results; If not, a prompt message for changing keywords or category labels is generated.

6. The method according to claim 5, characterized in that Determining whether the target keyword and the target category label meet the relevance requirement includes: Calculating the semantic similarity between the target keyword and the field corresponding to the target category label; Determine one by one whether the semantic similarities are all less than a similarity threshold; If not, the target keyword and the target category label meet the relevance requirement; If so, the target keyword and the target category label do not meet the relevance requirement.

7. The method according to claim 1, characterized in that Displaying the information search results in the result display area according to the preset display rules includes: Performing quality assessment and summary generation on each result in the information search results to obtain a quality score and content summary corresponding to each result in the information search results; Determining results to be displayed and results to be hidden in the information search results according to a preset display quantity and the quality score; The quality score and the content summary corresponding to the result to be displayed are expanded and displayed in the first partition of the result display area, and the quality score and the content summary corresponding to the result to be hidden are folded and displayed in the second partition of the result display area.

8. An information search device, characterized in that: The device comprises: An acquisition unit, used to acquire information search parameters customized by the user in the search configuration area, wherein the information search parameters at least include a target category label corresponding to a category filter item of a dynamically updated category and a target keyword corresponding to a keyword input item of a dynamically recommended vocabulary; A first processing unit, configured to generate an information search request based on the target category label and the target keyword obtained by the acquisition unit, and send the information search request to a database, so that the database performs a corresponding search operation based on the information search request and obtains information search results; A display unit is used to display the information search results obtained by the first processing unit in a result display area according to a preset display rule, wherein the preset display rule is used to represent a display rule for expanding or collapsing each result in the information search results according to a preset number.

9. A storage medium, characterized in that: The storage medium includes a stored program, wherein when the program is executed, the device where the storage medium is located is controlled to execute the information search method according to any one of claims 1 to 7.

10. A processor, characterized in that: The processor is used to run a program, wherein the program executes the information search method according to any one of claims 1 to 7 when running.

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