Information recommendation method and device, computer equipment and storage medium

Abstract

The embodiment of the invention belongs to the technical field of intelligent decision making, and relates to an information recommendation method and device, computer equipment and a storage medium, and the method comprises the steps: receiving an information recommendation request which is sent by a user terminal and carries problem seeking information; acquiring user historical data of a requesting user according to the request; performing screening operation on an information list according to the problem seeking information, the user historical data and a map-reduce hierarchical screening algorithm to obtain target information data; and outputting the target information data to the user terminal. According to the method, the interpretability of the recommendation result is enhanced, and the credibility and satisfaction of the user on recommendation are improved.

Description

Technical Field

[0001] The present application relates to the field of intelligent decision-making technology, and in particular to an information recommendation method, device, computer equipment and storage medium. Background Art

[0002] The emergence and popularization of the Internet has brought a large amount of information to users, satisfying their needs for information in the information age. However, it has also led to the fact that users are unable to obtain the information that is truly useful to them when faced with a large amount of information.

[0003] A very effective way to solve the above problems is the recommendation system, which is a personalized information recommendation system that recommends information and products of interest to users based on their information needs and interests. Most of the current information recommendation systems are based on generative large language models, which can generate human-like responses.

[0004] However, the applicant found that the traditional recommendation system cannot fully consider the user's personalized information and needs, which reduces the user's trust in the recommendation results. This shows that the traditional recommendation system has the problem of low accuracy. Summary of the invention

[0005] The purpose of the embodiments of the present application is to provide an information recommendation method, apparatus, computer device and storage medium to solve the problem of low accuracy in traditional recommendation systems.

[0006] In order to solve the above technical problems, the present application embodiment provides an information recommendation method, which adopts the following technical solution:

[0007] Receiving an information recommendation request sent by a user terminal and carrying a question seeking information;

[0008] Acquire user history data of the requesting user according to the request;

[0009] Filter the information list according to the information seeking question, the user historical data and the map-reduce hierarchical filtering algorithm to obtain target information data;

[0010] The target information data is output to the user terminal.

[0011] Furthermore, the step of filtering the information list according to the question for information seeking, the user historical data and the map-reduce hierarchical filtering algorithm to obtain the target information data specifically includes the following steps:

[0012] Performing feature extraction on the question-seeking information to obtain a user intention feature vector;

[0013] Obtain the feature vector of each information in the candidate information list;

[0014] Calculating a similarity score between the feature vector and the user intention feature vector;

[0015] The similarity scores are screened according to a preset similarity threshold to obtain the target information data.

[0016] Furthermore, the step of filtering the similarity scores according to a preset similarity threshold to obtain the target information data specifically includes the following steps:

[0017] Performing a screening operation on the similarity score according to a preset similarity threshold to obtain a target similarity score and screening information corresponding to the target similarity score;

[0018] Performing a hierarchical clustering operation on the screening information according to a hierarchical clustering algorithm and the similarity score to obtain a clustering hierarchy;

[0019] In the same clustering level, the screening information in the clustering level is sorted according to the information relevance, and the screening information with the highest sorting is used as the representative information of the clustering level;

[0020] In all clustering levels, the representative information of each clustering level is sorted according to the level and the relevance of the representative information to obtain the target information data.

[0021] Furthermore, after the step of obtaining the user history data of the requesting user according to the request, the following steps are also included:

[0022] According to the preset data quality standards, the user historical data is cleaned using the MapReduce distributed computing framework to obtain a preliminary data set;

[0023] Performing feature extraction operations on the preliminary data set according to the MapReduce distributed computing framework to obtain key feature data;

[0024] The step of filtering the information list according to the question for information seeking, the user historical data and the map-reduce hierarchical filtering algorithm to obtain the target information data specifically includes the following steps:

[0025] The information list is screened according to the information sought in the question, the key feature data and the map-reduce hierarchical screening algorithm to obtain target information data.

[0026] Furthermore, the step of filtering the information list according to the question for information seeking, the user historical data and the map-reduce hierarchical filtering algorithm to obtain the target information data specifically includes the following steps:

[0027] According to the information seeking question and the user history data, a matching algorithm for filtering keywords is used to obtain a matching score for each piece of information in the information list to obtain a first filtering result;

[0028] Extracting information feature vectors from the first screening results according to a clustering algorithm and grouping them, calculating user preferences with the user history data, and obtaining a second screening result;

[0029] The information in the second screening result is sorted according to the comprehensive weight score, and the information in the second screening result is screened comprehensively according to timeliness and authority to determine the target information data.

[0030] In order to solve the above technical problems, the embodiment of the present application further provides an information recommendation device, which adopts the following technical solution:

[0031] A request acquisition module, used for receiving an information recommendation request carrying question-seeking information sent by a user terminal;

[0032] A historical data acquisition module, used to acquire user historical data of the requesting user according to the request;

[0033] An information screening module, used to screen the information list according to the information seeking question, the user historical data and the map-reduce hierarchical screening algorithm to obtain target information data;

[0034] An output module is used to output the target information data to the user terminal.

[0035] Furthermore, the information screening module includes:

[0036] The intention feature acquisition submodule is used to perform feature extraction operations on the question-seeking information to obtain a user intention feature vector;

[0037] An information feature acquisition submodule is used to obtain a feature vector of each information in the candidate information list;

[0038] A similarity calculation submodule, used to calculate a similarity score between the feature vector and the user intention feature vector;

[0039] The screening submodule is used to screen the similarity scores according to a preset similarity threshold to obtain the target information data.

[0040] Furthermore, the screening submodule includes:

[0041] A screening unit, configured to perform a screening operation on the similarity score according to a preset similarity threshold to obtain a target similarity score and screening information corresponding to the target similarity score;

[0042] A hierarchical clustering unit, used for performing a hierarchical clustering operation on the screening information according to a hierarchical clustering algorithm and the similarity score to obtain a clustering hierarchy;

[0043] A first sorting unit is used to sort the screening information in the same clustering level according to the information relevance, and use the screening information with the highest sorting as the representative information of the clustering level;

[0044] The second sorting unit is used to sort the representative information of each clustering level according to the level of the level and the relevance of the representative information in all clustering levels to obtain the target information data.

[0045] In order to solve the above technical problems, the embodiment of the present application further provides a computer device, which adopts the following technical solution:

[0046] The method comprises a memory and a processor, wherein the memory stores computer-readable instructions, and the processor implements the steps of the information recommendation method as described above when executing the computer-readable instructions.

[0047] In order to solve the above technical problems, the embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solution:

[0048] The computer-readable storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the steps of the information recommendation method described above are implemented.

[0049] The present application provides an information recommendation method, including: receiving an information recommendation request carrying question-seeking information sent by a user terminal; obtaining user history data of the requesting user according to the request; filtering the information list according to the question-seeking information, the user history data and the map-reduce hierarchical filtering algorithm to obtain target information data; and outputting the target information data to the user terminal. Compared with the prior art, the present application improves the personalization of information recommendation and meets the diverse needs of users; improves the accuracy and efficiency of recommendation, saves users' time and energy; enhances the interpretability of recommendation results, and improves users' trust and satisfaction with the recommendation. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the scheme in the present application, a brief introduction is given below to the drawings required for use in the description of the embodiments of the present application. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0051] Figure 1 is an exemplary system architecture diagram to which the present application may be applied;

[0052] Figure 2 This is a flowchart of the implementation of the information recommendation method provided in Example 1 of the present application;

[0053] Figure 3 is a schematic diagram of the structure of the information recommendation device provided in Embodiment 2 of the present application;

[0054] Figure 4 It is a structural diagram of an embodiment of a computer device according to the present application. DETAILED DESCRIPTION

[0055] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of the present application; the terms used in the specification of the application herein are only for the purpose of describing specific embodiments and are not intended to limit the present application; the terms "including" and "having" and any variations thereof in the specification and claims of the present application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of the present application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0056] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0057] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0058] like Figure 1As shown, the system architecture 100 may include a terminal device 101, a network 102 and a server 103. The terminal device 101 may be a laptop 1011, a tablet computer 1012 or a mobile phone 1013. The network 102 is used to provide a medium for a communication link between the terminal device 101 and the server 103. The network 102 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0059] The user can use the terminal device 101 to interact with the server 103 through the network 102 to receive or send messages, etc. Various communication client applications can be installed on the terminal device 101, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0060] The terminal device 101 can be any electronic device with a display screen and supporting web browsing. In addition to a laptop computer 1011, a tablet computer 1012 or a mobile phone 1013, the terminal device 101 can also be an e-book reader, an MP3 player (Moving Picture Experts Group Audio Layer III), an MP4 player (Moving Picture Experts Group Audio Layer IV), a laptop computer, a desktop computer, etc.

[0061] The server 103 may be a server that provides various services, such as a background server that provides support for a web page displayed on the terminal device 101 .

[0062] It should be noted that the information recommendation method provided in the embodiment of the present application is generally executed by a server / terminal device, and accordingly, the information recommendation device is generally arranged in the server / terminal device.

[0063] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0064] Embodiment 1

[0065] Continue to refer Figure 2 , shows a flow chart of an embodiment of the information recommendation method according to the present application. The information recommendation method comprises: step S201, step S202, step S203 and step S204.

[0066] In step S201, an information recommendation request carrying question-seeking information sent by a user terminal is received.

[0067] In the embodiments of the present application, the user terminal refers to a terminal device used to execute the image processing method for preventing document abuse provided by the present application. The user terminal can be a mobile terminal such as a mobile phone, a smart phone, a laptop computer, a digital broadcast receiver, a PDA (personal digital assistant), a PAD (tablet computer), a PMP (portable multimedia player), a navigation device, etc., as well as a fixed terminal such as a digital TV, a desktop computer, etc. It should be understood that the examples of user terminals here are only for the convenience of understanding and are not used to limit the present application.

[0068] In the embodiment of the present application, when a user asks a relevant question to seek information, the information recommendation task is entered after intent recognition.

[0069] In step S202, user history data of the requesting user is obtained according to the request.

[0070] In an embodiment of the present application, after receiving a user's request, the present application will attempt to obtain the user's historical data, which may include the user's past search history, click behavior, purchase history, browsing history, etc., for analyzing the user's interests, preferences and behavior patterns.

[0071] In step S203, the information list is screened according to the question-seeking information, user historical data and the map-reduce hierarchical screening algorithm to obtain target information data.

[0072] In an embodiment of the present application, the recommendation task filters the product list based on user information, orders, operation history, demand and other data, wherein a map-reduce hierarchical screening algorithm is used to improve the recommendation efficiency.

[0073] In an embodiment of the present application, the system will seek information based on the user's current problem, the user's historical data, and a specific filtering algorithm (here is the map-reduce hierarchical filtering algorithm) to filter out the content that best meets the user's needs from a large amount of information. Among them, Map-reduce is a programming model for parallel computing of large data sets, which is very suitable for processing information filtering tasks of large data sets.

[0074] In step S204, the target information data is output to the user terminal.

[0075] In the embodiment of the present application, the system sends the filtered target information data back to the user's device, and the user can see the relevant information recommended for his or her problem or need. This information may be articles, videos, product recommendations or any other form of content.

[0076] In some optional implementation methods of the embodiments of the present application, the present application also builds an operation intervention system to support operation personnel to intervene in and optimize the recommendation model, including sorting, information supplementation and other means.

[0077] In some optional implementations of the embodiments of the present application, the model outputs the result of the product recommendation, and also outputs the thinking process and reasons for the recommendation.

[0078] In some optional implementations of the embodiments of the present application, after the target information data is obtained, the target information data can be evaluated and optimized through an integration and self-reflection module.

[0079] In an embodiment of the present application, an information recommendation method is provided, including: receiving an information recommendation request carrying question-seeking information sent by a user terminal; obtaining user history data of the requesting user according to the request; filtering the information list according to the question-seeking information, user history data and map-reduce hierarchical filtering algorithm to obtain target information data; outputting the target information data to the user terminal. Compared with the prior art, the present application improves the personalization of information recommendation and meets the diverse needs of users; improves the accuracy and efficiency of recommendation, saves users' time and energy; enhances the interpretability of recommendation results, and improves users' trust and satisfaction with the recommendation.

[0080] In some optional implementations of the embodiments of the present application, the above step S203 specifically includes the following steps:

[0081] Performing feature extraction on the question-seeking information to obtain a user intention feature vector;

[0082] Obtain the feature vector of each information in the candidate information list;

[0083] Calculating a similarity score between the feature vector and the user intention feature vector;

[0084] The similarity scores are screened according to a preset similarity threshold to obtain the target information data.

[0085] In the embodiment of the present application, the cosine similarity can be used to calculate the similarity score between the candidate information and the user intention feature vector. As an example, for example, assuming that the user intention feature vector is [2,5,3] and the feature vector of the candidate information A is [1,6,3], the similarity score is 2*1+5*6+3*3=41.

[0086] In the embodiment of the present application, candidate information with a similarity score lower than the preset threshold can be eliminated according to the preset threshold.

[0087] In some optional implementations of the embodiments of the present application, the above step of performing a screening operation on the similarity scores according to a preset similarity threshold to obtain the target information data specifically includes the following steps:

[0088] Performing a screening operation on the similarity score according to a preset similarity threshold to obtain a target similarity score and screening information corresponding to the target similarity score;

[0089] Performing a hierarchical clustering operation on the screening information according to a hierarchical clustering algorithm and the similarity score to obtain a clustering hierarchy;

[0090] In the same clustering level, the screening information in the clustering level is sorted according to the information relevance, and the screening information with the highest sorting is used as the representative information of the clustering level;

[0091] In all clustering levels, the representative information of each clustering level is sorted according to the level and the relevance of the representative information to obtain the target information data.

[0092] In the embodiment of the present application, the Ward method may be used for hierarchical clustering. As an example, for example, information with a similarity greater than 7 is clustered into one category.

[0093] In the embodiment of the present application, the information with the highest correlation degree can be selected in each clustering level as the representative of the level.

[0094] In the embodiment of the present application, among different levels, weights can be assigned by comprehensively considering the level and the relevance of representative information to perform weighted summing and sorting.

[0095] In some optional implementations of the embodiments of the present application, after step S202, the following steps are further included:

[0096] According to the preset data quality standards, the user historical data is cleaned using the MapReduce distributed computing framework to obtain a preliminary data set;

[0097] Performing feature extraction operations on the preliminary data set according to the MapReduce distributed computing framework to obtain key feature data;

[0098] Step S203 specifically includes the following steps:

[0099] The information list is screened according to the information sought in the question, the key feature data and the map-reduce hierarchical screening algorithm to obtain target information data.

[0100] In an embodiment of the present application, the MapReduce distributed computing framework is used to clean user historical data according to preset data quality standards. As an example, regular expressions are used to match and filter noise data, duplicate data is removed through data deduplication algorithms such as Bloom filters, and validity rules are set according to data characteristics to filter invalid data, ultimately obtaining a high-quality preliminary data set of tens of millions of levels.

[0101] In an embodiment of the present application, feature extraction is performed on the cleaned data set through a MapReduce task, and a user-item interaction matrix and an item attribute vector are constructed using user behavior log data and item attribute metadata. Behavioral features such as user clicks, favorites, and purchases, as well as attribute features such as item category, price, and brand are extracted to form structured feature data with hundreds of dimensions.

[0102] In the embodiment of the present application, using the MapReduce distributed computing framework for data cleaning operations is an efficient choice. Through its distributed processing capabilities, the MapReduce framework can effectively process massive amounts of data in parallel, thereby significantly improving data processing efficiency. In the data cleaning stage, the framework can perform operations such as removing duplicate records, correcting erroneous data, and filling missing values ​​to ensure the integrity and consistency of the data. Through these steps, a preliminary data set with higher quality and more suitable for further analysis can be obtained.

[0103] In an embodiment of the present application, performing feature extraction on a preliminary data set based on a MapReduce framework is a key step in data analysis. Feature extraction refers to the process of extracting the most useful information or attributes for solving a problem from raw data. Under the MapReduce framework, this operation can be performed on multiple nodes in parallel, with each node processing a portion of the data set, thereby greatly speeding up feature extraction. Through feature extraction, key feature data can be extracted from the preliminary data set, which is essential for subsequent machine learning model training, user behavior analysis, trend prediction and other tasks. In short, using the MapReduce framework for feature extraction can not only efficiently process large-scale data sets, but also extract key feature data that is most valuable for business analysis.

[0104] In some optional implementations of the embodiments of the present application, the above step S203 specifically includes the following steps:

[0105] According to the information seeking question and the user history data, a matching algorithm for filtering keywords is used to obtain a matching score for each piece of information in the information list to obtain a first filtering result;

[0106] Extracting information feature vectors from the first screening results according to a clustering algorithm and grouping them, calculating user preferences with the user history data, and obtaining a second screening result;

[0107] The information in the second screening result is sorted according to the comprehensive weight score, and the information in the second screening result is screened comprehensively according to timeliness and authority to determine the target information data.

[0108] In an embodiment of the present application, first, based on the questions raised by the user and the user's historical data (such as past search records, click behaviors, etc.), the system uses a matching algorithm for screening keywords to process each piece of information in the information list, wherein the matching algorithm calculates the matching score of each piece of information with the user's questions and historical data, that is, evaluates the relevance of the information to the user's needs and interests.

[0109] In the embodiment of the present application, the first screening result is a list of information sorted by matching scores.

[0110] In an embodiment of the present application, after obtaining the first screening result, the system uses a clustering algorithm to further process the information in the first screening result. Specifically, the clustering algorithm extracts the feature vector of each piece of information (i.e., the core features or attributes of the information) and groups the information according to these feature vectors.

[0111] In the embodiment of the present application, the system calculates the user's preference for each set of information based on the user's historical data, that is, evaluates the user's interest in each set of information.

[0112] In the embodiment of the present application, the second screening result is information grouping sorted by user preference.

[0113] In an embodiment of the present application, the system will sort the information in the second screening result according to the comprehensive weight score, where the comprehensive weight score may take into account multiple factors, such as the matching degree of the information, user preference, timeliness of the information (i.e., the freshness of the information) and authority (i.e., the credibility of the information and the reliability of the source).

[0114] In the embodiment of the present application, the system will screen the information based on its comprehensive performance and ultimately determine the target information data, that is, the information that best meets the user's needs and interests.

[0115] In the embodiment of the present application, the above process is a multi-level and multi-dimensional information screening process, which aims to screen out target information data that best meets user needs and interests from a large amount of information.

[0116] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Artificial Intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0117] AI basic technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics, etc. AI software technologies mainly include computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0118] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through computer-readable instructions, and the computer-readable instructions can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the aforementioned storage medium can be a non-volatile storage medium such as a disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0119] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0120] Embodiment 2

[0121] Further references Figure 3 , as a response to the above Figure 2 The present application provides an embodiment of an information recommendation device, and the device embodiment is similar to Figure 2 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0122] like Figure 3As shown, the information recommendation device 200 of the embodiment of the present application includes:

[0123] The request acquisition module 210 is used to receive an information recommendation request carrying question-seeking information sent by a user terminal;

[0124] A historical data acquisition module 220, used to acquire user historical data of a requesting user according to a request;

[0125] The information screening module 230 is used to screen the information list according to the information sought by the question, the user's historical data and the map-reduce hierarchical screening algorithm to obtain the target information data;

[0126] The output module 240 is used to output target information data to the user terminal.

[0127] In the embodiment of the present application, an information recommendation device 200 is provided, including: a request acquisition module 210, for receiving an information recommendation request carrying question-seeking information sent by a user terminal; a historical data acquisition module 220, for acquiring the user history data of the requesting user according to the request; an information screening module 230, for screening the information list according to the question-seeking information, the user history data and the map-reduce hierarchical screening algorithm to obtain the target information data; an output module 240, for outputting the target information data to the user terminal. Compared with the prior art, the present application improves the personalization of information recommendation and meets the diverse needs of users; improves the accuracy and efficiency of recommendation, saves the time and energy of users; enhances the interpretability of recommendation results, and improves the trust and satisfaction of users in recommendations.

[0128] In some optional implementations of the embodiments of the present application, the information screening module includes:

[0129] The intention feature acquisition submodule is used to perform feature extraction operations on the question-seeking information to obtain a user intention feature vector;

[0130] An information feature acquisition submodule is used to obtain a feature vector of each information in the candidate information list;

[0131] A similarity calculation submodule, used to calculate a similarity score between the feature vector and the user intention feature vector;

[0132] The screening submodule is used to screen the similarity scores according to a preset similarity threshold to obtain the target information data.

[0133] In some optional implementations of the embodiments of the present application, the screening submodule includes:

[0134] A screening unit, configured to perform a screening operation on the similarity score according to a preset similarity threshold to obtain a target similarity score and screening information corresponding to the target similarity score;

[0135] A hierarchical clustering unit, used for performing a hierarchical clustering operation on the screening information according to a hierarchical clustering algorithm and the similarity score to obtain a clustering hierarchy;

[0136] A first sorting unit is used to sort the screening information in the same clustering level according to the information relevance, and use the screening information with the highest sorting as the representative information of the clustering level;

[0137] The second sorting unit is used to sort the representative information of each clustering level according to the level of the level and the relevance of the representative information in all clustering levels to obtain the target information data.

[0138] To solve the above technical problems, the present application also provides a computer device. Figure 4 , Figure 4 This is a basic structural block diagram of a computer device according to an embodiment of the present application.

[0139] The computer device 300 includes a memory 310, a processor 320, and a network interface 330 that are interconnected and communicated through a system bus. It should be noted that the figure only shows a computer device 300 having components 310-330, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital processors (DSPs), embedded devices, etc.

[0140] The computer device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The computer device may interact with a user through a keyboard, a mouse, a remote controller, a touch pad, or a voice control device.

[0141] The memory 310 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 310 can be an internal storage unit of the computer device 300, such as a hard disk or memory of the computer device 300. In other embodiments, the memory 310 can also be an external storage device of the computer device 300, such as a plug-in hard disk equipped on the computer device 300, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card, etc. Of course, the memory 310 can also include both the internal storage unit of the computer device 300 and its external storage device. In the embodiment of the present application, the memory 310 is generally used to store the operating system and various application software installed on the computer device 300, such as computer-readable instructions of the information recommendation method, etc. In addition, the memory 310 can also be used to temporarily store various data that have been output or will be output.

[0142] The processor 320 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip in some embodiments. The processor 320 is generally used to control the overall operation of the computer device 300. In the embodiment of the present application, the processor 320 is used to run the computer-readable instructions stored in the memory 310 or process data, such as computer-readable instructions for running the information recommendation method.

[0143] The network interface 330 may include a wireless network interface or a wired network interface. The network interface 330 is generally used to establish a communication connection between the computer device 300 and other electronic devices.

[0144] The computer device provided by this application improves the personalization level of information recommendation and meets the diverse needs of users; improves the accuracy and efficiency of recommendation and saves users' time and energy; enhances the interpretability of recommendation results and improves users' trust and satisfaction with the recommendation.

[0145] The present application also provides another embodiment, namely, providing a computer-readable storage medium, wherein the computer-readable storage medium stores computer-readable instructions, and the computer-readable instructions can be executed by at least one processor to enable the at least one processor to perform the steps of the information recommendation method as described above.

[0146] The computer-readable storage medium provided by the present application improves the personalization level of information recommendations and meets the diverse needs of users; improves the accuracy and efficiency of recommendations and saves users’ time and energy; enhances the interpretability of recommendation results and improves users’ trust and satisfaction with recommendations.

[0147] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0148] Obviously, the embodiments described above are only some embodiments of the present application, rather than all embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application is described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions recorded in the aforementioned specific implementation methods, or to perform equivalent replacement of some of the technical features therein. Any equivalent structure made using the contents of the specification and drawings of this application, directly or indirectly used in other related technical fields, is similarly within the scope of patent protection of this application.

Claims

1. An information recommendation method, characterized in that: The steps include: Receiving an information recommendation request sent by a user terminal and carrying a question seeking information; Acquire user history data of the requesting user according to the request; Filter the information list according to the information seeking question, the user historical data and the map-reduce hierarchical filtering algorithm to obtain target information data; The target information data is output to the user terminal.

2. The information recommendation method according to claim 1, characterized in that: The step of filtering the information list according to the question for information seeking, the user historical data and the map-reduce hierarchical filtering algorithm to obtain the target information data specifically includes the following steps: Performing feature extraction on the question-seeking information to obtain a user intention feature vector; Obtain the feature vector of each information in the candidate information list; Calculating a similarity score between the feature vector and the user intention feature vector; The similarity scores are screened according to a preset similarity threshold to obtain the target information data.

3. The information recommendation method according to claim 2, characterized in that: The step of filtering the similarity scores according to a preset similarity threshold to obtain the target information data specifically includes the following steps: Performing a screening operation on the similarity score according to a preset similarity threshold to obtain a target similarity score and screening information corresponding to the target similarity score; Performing a hierarchical clustering operation on the screening information according to a hierarchical clustering algorithm and the similarity score to obtain a clustering hierarchy; In the same clustering level, the screening information in the clustering level is sorted according to the information relevance, and the screening information with the highest sorting is used as the representative information of the clustering level; In all clustering levels, the representative information of each clustering level is sorted according to the level and the relevance of the representative information to obtain the target information data.

4. The information recommendation method according to claim 1, characterized in that: After the step of obtaining the user history data of the requesting user according to the request, the following steps are also included: According to the preset data quality standards, the user historical data is cleaned using the MapReduce distributed computing framework to obtain a preliminary data set; Performing feature extraction operations on the preliminary data set according to the MapReduce distributed computing framework to obtain key feature data; The step of filtering the information list according to the question for information seeking, the user historical data and the map-reduce hierarchical filtering algorithm to obtain the target information data specifically includes the following steps: The information list is screened according to the information sought in the question, the key feature data and the map-reduce hierarchical screening algorithm to obtain target information data.

5. The information recommendation method according to claim 1, characterized in that: The step of filtering the information list according to the question for information seeking, the user historical data and the map-reduce hierarchical filtering algorithm to obtain the target information data specifically includes the following steps: According to the information seeking question and the user history data, a matching algorithm for filtering keywords is used to obtain a matching score for each piece of information in the information list to obtain a first filtering result; Extracting information feature vectors from the first screening results according to a clustering algorithm and grouping them, calculating user preferences with the user history data, and obtaining a second screening result; The information in the second screening result is sorted according to the comprehensive weight score, and the information in the second screening result is screened comprehensively according to timeliness and authority to determine the target information data.

6. An information recommendation device, characterized in that: include: A request acquisition module, used for receiving an information recommendation request carrying question-seeking information sent by a user terminal; A historical data acquisition module, used to acquire user historical data of the requesting user according to the request; An information screening module, used to screen the information list according to the information seeking question, the user historical data and the map-reduce hierarchical screening algorithm to obtain target information data; An output module is used to output the target information data to the user terminal.

7. The information recommendation device according to claim 6, characterized in that: The information screening module comprises: The intention feature acquisition submodule is used to perform feature extraction operations on the question-seeking information to obtain a user intention feature vector; An information feature acquisition submodule is used to obtain a feature vector of each information in the candidate information list; A similarity calculation submodule, used to calculate a similarity score between the feature vector and the user intention feature vector; The screening submodule is used to perform a screening operation on the similarity scores according to a preset similarity threshold to obtain the target information data.

8. The information recommendation device according to claim 7, characterized in that: The screening submodule comprises: A screening unit, configured to perform a screening operation on the similarity score according to a preset similarity threshold to obtain a target similarity score and screening information corresponding to the target similarity score; A hierarchical clustering unit, used for performing a hierarchical clustering operation on the screening information according to a hierarchical clustering algorithm and the similarity score to obtain a clustering hierarchy; A first sorting unit is used to sort the screening information in the same clustering level according to the information relevance, and use the screening information with the highest sorting as the representative information of the clustering level; The second sorting unit is used to sort the representative information of each clustering level according to the level of the level and the relevance of the representative information in all clustering levels to obtain the target information data.

9. A computer device comprising a memory and a processor, characterized in that: The memory stores computer-readable instructions, and when the processor executes the computer-readable instructions, the steps of the information recommendation method according to any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by a processor, the steps of the information recommendation method according to any one of claims 1 to 6 are implemented.

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