Object recommendation method and object recommendation apparatus, electronic device, storage medium

Abstract

The embodiment of the application provides a kind of object recommendation method and object recommendation device, electronic equipment, storage medium, belong to the technical field of financial technology.The method comprises the following steps: obtaining the content interaction data obtained by target object in application to target promotion content interaction;Content interaction data is used to obtain the content interaction object of target object;Object category detection is carried out to content interaction object based on content interaction sub data, and the interaction object category is obtained;The candidate object feature of content interaction object and the historical interaction data of content interaction object in application are obtained;The preference matching score of content interaction object and target promotion content is determined based on historical interaction data;Object conversion prediction is carried out based on interaction object category, candidate object feature and preference matching score, and the target conversion score of content interaction object is obtained;Object recommendation is carried out to target object based on target conversion score.The embodiment of the application can improve the accuracy and efficiency of object recommendation.

Description

Technical Field

[0001] The present application relates to the field of financial technology, and in particular to an object recommendation method and object recommendation device, electronic device, and storage medium. Background Art

[0002] Recommendation refers to the process of recommending potential partners to a target user, thereby increasing the relevance of the application's inter-object relationships. For example, in a FinTech scenario involving adding salespeople, the recommended partners could be partners with whom the target user is likely to become friends. Alternatively, in a friend-adding scenario, the recommended partners could be partners with whom the target user is likely to become friends.

[0003] At present, in the scenario of recommending objects for specific occupations, objects that are closely related to the target object (i.e., objects belonging to a specific occupation) can usually be used as candidates, and recommendations can be made based on the results of interviews with the candidates. Alternatively, candidates that may be of interest can be selected through the Internet, and these candidates can be sorted by a single standard to be recommended and displayed to the target object. However, the first method is prone to situations where the attendance rate or participation of candidates is not high, which reduces the efficiency of object recommendation. In addition, due to specific occupational requirements, multiple factors need to be considered when selecting objects. The second method has poor accuracy in object recommendation for this scenario and cannot effectively promote the conversion rate of recommendation processing (i.e., the probability that the target object establishes an effective association with the recommended object after being recommended to the target object). Therefore, how to improve the accuracy and efficiency of object recommendation has become a technical problem that needs to be solved urgently. Summary of the Invention

[0004] The main purpose of the embodiments of the present application is to propose an object recommendation method and an object recommendation device, an electronic device, and a storage medium, aiming to improve the accuracy and efficiency of object recommendation.

[0005] To achieve the above objectives, a first aspect of an embodiment of the present application provides an object recommendation method, the method comprising:

[0006] Obtaining content interaction data obtained by a target subject interacting with target promotion content in an application, wherein the target promotion content includes a preset promotion category;

[0007] Acquire a content interaction object of the target object based on the content interaction data, wherein the content interaction data includes content interaction sub-data of the content interaction object;

[0008] Performing object category detection on the content interaction object based on the content interaction sub-data to obtain an interaction object category, where the interaction object category is used to indicate an emotional direction of the interaction between the content interaction object and the target promotional content in the application;

[0009] obtaining candidate object features of the content interaction object and historical interaction data of the content interaction object in the application;

[0010] determining a preference matching score of the content interaction object and the target promotion content based on the historical interaction data;

[0011] performing object conversion prediction based on the interaction object category, the candidate object features and the preference matching score to obtain a target conversion score of the content interaction object, the target conversion score being used to represent a degree of conversion of the content interaction object into an object matching the preset promotion category;

[0012] performing object recommendation to the target object based on the target conversion score.

[0013] In some embodiments, the object recommendation to the target object based on the target conversion score comprises:

[0014] performing grade matching based on the target conversion score and a preset score grade to obtain a recommended object grade of the content interaction object;

[0015] performing sorting on the content interaction object based on the recommended object grade and the target conversion score to obtain a recommended object sequence;

[0016] selecting a target recommended object from the recommended object sequence based on a preset recommended quantity, and recommending the target recommended object to the target object.

[0017] In some embodiments, the determination of the preference matching score of the content interaction object and the target promotion content based on the historical interaction data comprises:

[0018] obtaining associated object data of the content interaction object from the historical interaction data;

[0019] determining a total number of associated objects, a total number of same attribute objects and a total number of common associated objects of the content interaction object based on the associated object data, the total number of associated objects being used to represent a number of associated objects of the content interaction object in the application, the total number of same attribute objects being used to represent a number of objects associated with the content interaction object in the application and being the same as the preset promotion category, and the total number of common associated objects being used to represent a number of objects commonly associated with the target object in the application;

[0020] determining an interaction frequency and a main interaction type of the content interaction object and the target object in the application based on the content interaction sub-data;

[0021] The preference matching score between the content interaction object and the target promotion content is determined based on the total number of associated objects, the total number of objects with the same attribute, the number of commonly associated objects, the interaction frequency, and the main interaction type.

[0022] In some embodiments, determining the preference matching score between the content interaction object and the target promotion content based on the total number of associated objects, the total number of objects with the same attribute, the number of commonly associated objects, the interaction frequency, and the primary interaction type includes:

[0023] Determining a first matching score of the content interaction object based on the total number of associated objects;

[0024] Determining a second matching score of the content interaction object based on the total number of objects with the same attribute;

[0025] determining a third matching score of the content interaction object based on the number of commonly associated objects;

[0026] determining a fourth matching score of the content interaction object based on the interaction frequency;

[0027] determining a fifth matching score of the content interaction object based on the primary interaction type;

[0028] The preference matching score between the content interaction object and the target promotion content is determined based on the first matching score, the second matching score, the third matching score, the fourth matching score, and the fifth matching score of the content interaction object.

[0029] In some embodiments, before obtaining content interaction data obtained by the target object interacting with the target promotion content in the application, the method further includes:

[0030] Get the object type of the target object and generate content conditional text;

[0031] Performing content generation on the object type and the content generation condition text based on a preset content generation model to obtain candidate promotion content, wherein the candidate promotion content is stored in a content library of the content generation model;

[0032] Select target promotion content from the candidate promotion content, and mark the target promotion content in the content library to update the priority of the target promotion content in the content library.

[0033] In some embodiments, performing object category detection on the content interaction object based on the content interaction sub-data to obtain the interactive object category includes:

[0034] obtain content interaction text from the content interaction sub-data;

[0035] perform text segmentation on the content interaction text to obtain content interaction words;

[0036] perform word vectorization on the content interaction words to obtain interaction word vectors;

[0037] obtain a preset word vector of a preset category keyword;

[0038] extract key features from the interaction word vectors to obtain interaction key word vectors;

[0039] perform vector similarity calculation on the preset word vector and the interaction key word vectors to obtain a word vector similarity;

[0040] determine the interaction object category based on the word vector similarity.

[0041] In some embodiments, the object conversion prediction based on the interaction object category, the candidate object features, and the preference matching score obtains a target conversion score of the content interaction object, including:

[0042] determine interaction strategy data for the content interaction object based on the interaction object category and the content interaction sub-data;

[0043] obtain object feedback data of the content interaction object on the interaction strategy data;

[0044] determine the target conversion score of the content interaction object based on the interaction object category, the candidate object features, the preference matching score, and the object feedback data.

[0045] To achieve the above-mentioned purposes, a second aspect of the embodiments of the present application proposes an object recommendation device, the device comprising:

[0046] a first obtaining module configured to obtain content interaction data of a target object in an application on a target promoted content, the target promoted content containing a preset promotion category;

[0047] an object obtaining module configured to obtain a content interaction object of the target object based on the content interaction data, the content interaction data including content interaction sub-data of the content interaction object;

[0048] a category detection module configured to perform object category detection on the content interaction object based on the content interaction sub-data to obtain an interaction object category, the interaction object category being used to indicate an emotional direction of the content interaction object interacting with the target promoted content in the application;

[0049] A second acquisition module is configured to acquire candidate object features of the content interaction object and historical interaction data of the content interaction object in the application;

[0050] a score determination module, configured to determine a preference matching score between the content interaction object and the target promotion content based on the historical interaction data;

[0051] An object conversion module is configured to predict object conversion based on the interactive object category, the candidate object features, and the preference matching score, and obtain a target conversion score for the content interactive object, wherein the target conversion score is used to represent the degree to which the content interactive object is converted into an object matching the preset promotion category;

[0052] A recommendation module is used to recommend an object to the target object based on the target conversion score.

[0053] To achieve the above-mentioned purpose, the third aspect of an embodiment of the present application proposes an electronic device, which includes a memory and a processor, the memory stores a computer program, and the processor implements the object recommendation method described in the first aspect when executing the computer program.

[0054] To achieve the above-mentioned purpose, the fourth aspect of the embodiments of the present application proposes a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the object recommendation method described in the first aspect above.

[0055] The present application proposes an object recommendation method and object recommendation device, electronic device, and storage medium, which first obtain content interaction data obtained by a target object interacting with target promotion content in an application, where the target promotion content includes a preset promotion category; further, based on the content interaction data, a content interaction object of the target object is obtained, where the content interaction data includes content interaction sub-data of the content interaction object; object category detection is performed on the content interaction object based on the content interaction sub-data to obtain an interaction object category, where the interaction object category is used to indicate the emotional direction of the interaction between the content interaction object and the target promotion content in the application; further, candidate object features of the content interaction object and historical interaction data of the content interaction object in the application are obtained; a preference matching score between the content interaction object and the target promotion content is determined based on the historical interaction data; further, object conversion prediction is performed based on the interaction object category, candidate object features, and preference matching score to obtain a target conversion score of the content interaction object, where the target conversion score is used to characterize the degree to which the content interaction object is converted into an object matching the preset promotion category; finally, an object is recommended to the target object based on the target conversion score. After determining the target object's content interaction object, the embodiment of the present application can first perform object category detection on the content interaction object, and then determine the target conversion score of the content interaction object based on the detected category and the determined preference matching score between the content interaction object and the target promotion content. Compared to related technologies that do not consider multiple factors related to the preset promotion category when selecting objects, the present application can fully consider multiple factors related to the target promotion content and the content interaction object itself by determining the preference matching score and the target conversion score, thereby improving the accuracy and efficiency of object recommendations. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 This is the first flow chart of the object recommendation method provided by the embodiment of the present application;

[0057] Figure 2 This is a second flow chart of the object recommendation method provided in an embodiment of the present application;

[0058] Figure 3 yes Figure 1 A flowchart of step S130 in FIG.

[0059] Figure 4 yes Figure 1 A flowchart of step S150 in FIG.

[0060] Figure 5 yes Figure 4 A flowchart of step S440 in FIG.

[0061] Figure 6 yes Figure 1 A flowchart of step S160 in FIG.

[0062] Figure 7 yes Figure 1 A flowchart of step S170 in FIG.

[0063] Figure 8 This is a schematic diagram of the structure of the object recommendation device provided in an embodiment of the present application;

[0064] Figure 9 This is a hardware structure diagram of the electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0065] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0066] It should be noted that although the device schematics illustrate functional module divisions and the flowcharts illustrate logical sequences, in certain circumstances, the steps shown or described may be performed in a sequence that differs from the module divisions in the device or the sequence in the flowcharts. The terms "first," "second," and so on, in the specification, claims, and drawings, are used to distinguish similar items and are not necessarily used to describe a specific sequence or precedence.

[0067] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application pertains. The terms used herein are for the purpose of describing the embodiments of this application only and are not intended to limit this application.

[0068] First, let’s analyze some of the terms used in this application:

[0069] Artificial intelligence (AI) is a new technical discipline that studies and develops theories, methods, technologies, and application systems for simulating, extending, and expanding human intelligence. A branch of computer science, AI seeks to understand the essence of intelligence and produce new intelligent machines that can respond in a manner similar to human intelligence. Research in this field includes robotics, speech recognition, image recognition, natural language processing, and expert systems. AI can simulate the information processes of human consciousness and thinking. It also encompasses the theories, methods, technologies, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, to perceive the environment, acquire knowledge, and use that knowledge to achieve optimal results.

[0070] Robotic Process Automation (RPA) is a technology that uses software robots or artificial intelligence workers to automate repetitive tasks. It is designed to automate repetitive, regular, and predictable business processes or tasks.

[0071] The Chat Generalized Language Model (ChatGLM) is a Transformer-based model that uses an autoregressive model to learn language patterns and generate coherent, natural responses based on context. ChatGLM can be pre-trained on large amounts of text data to learn language patterns and generate text.

[0072] Natural Language Processing (NLP) is a key branch of artificial intelligence that aims to enable computers to understand and process human language. NLP technology enables natural interaction with humans by analyzing, understanding, and generating human language.

[0073] Recommendation refers to the process of recommending potential partners to a target user, thereby increasing the relevance of the application's inter-object relationships. For example, in a FinTech scenario involving adding salespeople, the recommended partners could be partners with whom the target user is likely to become friends. Alternatively, in a friend-adding scenario, the recommended partners could be partners with whom the target user is likely to become friends.

[0074] At present, in the scenario of recommending objects for specific occupations, objects that are closely related to the target object (i.e., objects belonging to a specific occupation) can usually be used as candidates, and recommendations can be made based on the results of interviews with the candidates. Alternatively, candidates that may be of interest can be selected through the Internet, and these candidates can be sorted by a single standard to be recommended and displayed to the target object. However, the first method is prone to situations where the attendance rate or participation of candidates is not high, which reduces the efficiency of object recommendation. In addition, due to specific occupational requirements, multiple factors need to be considered when selecting objects. The second method has poor accuracy in object recommendation for this scenario and cannot effectively promote the conversion rate of recommendation processing (i.e., the probability that the target object establishes an effective association with the recommended object after being recommended to the target object). Therefore, how to improve the accuracy and efficiency of object recommendation has become a technical problem that needs to be solved urgently.

[0075] Based on this, embodiments of the present application provide an object recommendation method and an object recommendation device, an electronic device, and a storage medium, which can improve the accuracy and efficiency of object recommendation.

[0076] The embodiments of the present application can acquire and process related data based on artificial intelligence technology. The artificial intelligence (AI) is a theory, method, technology and application system for simulating, extending and expanding human intelligence by using a digital computer or a machine controlled by a digital computer, perceiving an environment, acquiring knowledge and using the knowledge to obtain optimal results.

[0077] The artificial intelligence basic technology generally includes technologies such as sensors, special artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operation / interaction system, mechatronics, etc. The artificial intelligence software technology mainly includes computer vision technology, robot technology, biometric technology, speech processing technology, natural language processing technology, and machine learning / deep learning, etc.

[0078] The object recommendation method provided by the embodiments of the present application relates to the field of artificial intelligence. The object recommendation method provided by the embodiments of the present application can be applied in a terminal, can also be applied in a server end, and can also be software running in a terminal or a server end. In some embodiments, the terminal can be a smart phone, a tablet computer, a notebook computer, a desktop computer, etc.; the server end can be configured as an independent physical server, can also be configured as a server cluster or a distributed system composed of multiple physical servers, can also be configured as a cloud server providing basic cloud computing services such as cloud service, cloud database, cloud computing, cloud function, cloud storage, network service, cloud communication, middleware service, domain name service, security service, content distribution network (CDN), and big data and artificial intelligence platform; and the software can be an application implementing the object recommendation method, etc., but is not limited to the above forms.

[0079] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld devices or portable devices, tablet devices, multi-processor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in a distributed computing environment, in which tasks are performed by remote processing devices connected by a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media, including storage devices.

[0080] It should be noted that in each specific embodiment of the present application, when it comes to the need to perform relevant processing based on data related to the identity or characteristics of the object, such as object attribute information, object identity information, object association information, etc., the permission or consent of the object will be obtained first, and the collection, use and processing of such data will comply with relevant laws, regulations and standards. In addition, when the embodiment of the present application needs to obtain the sensitive personal information of the object, the separate permission or separate consent of the object will be obtained through a pop-up window or by jumping to a confirmation page. After clearly obtaining the separate permission or separate consent of the object, the necessary object-related data for the normal operation of the embodiment of the present application will be obtained.

[0081] See also Figure 1 , Figure 1 This is an optional flow chart of the object recommendation method provided in the embodiment of the present application. In some embodiments, Figure 1 The method may include but is not limited to steps S110 to S170:

[0082] Step S110, obtaining content interaction data obtained by the target object interacting with the target promotion content in the application;

[0083] Step S120, obtaining a content interaction object of the target object based on the content interaction data;

[0084] Step S130, performing object category detection on the content interaction object based on the content interaction sub-data to obtain the interaction object category;

[0085] Step S140 , obtaining candidate object features of the content interactive object and historical interaction data of the content interactive object in the application;

[0086] Step S150, determining a preference matching score between the content interaction object and the target promotion content based on the historical interaction data;

[0087] Step S160 , performing object conversion prediction based on the interactive object category, candidate object features, and preference matching score to obtain a target conversion score for the content interactive object;

[0088] Step S170: recommending an object to a target object based on the target conversion score.

[0089] In step S110 of some embodiments, the target object refers to the object for which the recommended object needs to be obtained. Application refers to software that can upload target promotion content, and the target promotion content can be pictures, videos, voice, etc., and its form is not specifically limited. For example, in the intelligent sales recruitment scenario of financial technology, in order to attract more objects who want to be called sales personnel, the target object can upload the obtained target promotion content to the application. The target promotion content here can be a motivational video related to the sales industry or entrepreneurship. In this way, other objects using the application can view the target promotion content, and can interact with the target promotion content (such as likes, comments, forwarding, etc.) to achieve interaction with the target object. Content interaction data refers to data generated by other objects using the application to interact with the target promotion content, such as specific comments on the target promotion content, whether to like, whether to forward, the number of forwardings, etc.

[0090] It should be noted that since the target object can upload more than one target promotion content in the application, the content interaction data at this time can refer to the data obtained by counting the interactions of all target promotion content uploaded by the target object, for example, all comments, all likes, all likes, all forwarded content, all forwarding times, etc. of the target promotion content by other objects using the application.

[0091] It should be noted that the target promotion content includes a preset promotion category, which can be used to indicate the key attribute characteristics of the target promotion content. For example, the target promotion content is mainly for selling items or attracting talents, etc., so that other objects viewing the target promotion content can quickly understand the promotion needs and goals of the content.

[0092] It should be noted that in order to allow more objects to view the target promotion content uploaded by the target object, this application can also use RPA to automatically interact with the content uploaded by the objects associated with the target object according to the preset strategy of the target object, such as forwarding and commenting, so as to obtain more other objects to view the target promotion content of the target object and improve the efficiency of object recommendation. For example, in the intelligent sales recruitment scenario of financial technology, the target object follows two objects in the application account, namely account object Z1 and account object Z2. This application can set RPA execution strategies through RPA, such as forwarding videos within 1 hour after the new video is released, leaving messages on the first 5 newly uploaded videos, etc. At this time, if account object Z1 uploads a video V1, the target object can automatically forward and leave messages within 1 hour after the video V1 is released.

[0093] See also Figure 2 , Figure 2This is another optional flow chart of the object recommendation method provided by the embodiment of the present application. In some embodiments, before step S110, the object recommendation method provided by the embodiment of the present application may further include steps S210 to S230:

[0094] Step S210, obtaining the object type of the target object and generating content condition text;

[0095] Step S220 , performing content generation on the object type and the content generation condition text based on a preset content generation model to obtain candidate promotion content;

[0096] Step S230 : Select target promotion content from the candidate promotion content, and mark the target promotion content in the content library to update the priority of the target promotion content in the content library.

[0097] In step S210 of some embodiments, the object type is used to characterize the object attribute characteristics of the target object or the content attribute characteristics of the target object's needs to be promoted. For example, the object attribute characteristics may include the object's professional attribute characteristics (such as salesperson, engineer, etc.), and the object content characteristics may characterize the key characteristics of the content used for promotion (such as entrepreneurship, recruitment, etc.). This can help the model understand how to generate content effectively. Generating content conditional text refers to related text condition information or demand information that affects content generation, for example, "Please generate entrepreneurial language for me, with the word count controlled within 30 words."

[0098] In step S220 of some embodiments, the present application can combine a pre-trained content generation model to generate content, and the content generation model can be constructed based on the model structure of ChatGLM, or based on model structures such as machine learning models and natural language generation models, without specific limitation. Specifically, the object type and the generated content condition text can be input into the content generation model, and content generation is performed on the object type and the generated content condition text based on the content generation model. These contents may cover different styles, tones and information to ensure that there is diversity to choose from. The generated candidate promotion content will be stored in the content library of the content generation model for subsequent use. Among them, the content library also stores historically generated promotion content to give the target object more content choices. For example, in a fintech intelligent recruitment scenario, to stimulate potential salespeople by showcasing their career development advantages, the target audience could be salespeople or HR professionals. The target audience's input type prompt could be "I am an insurance agent," and the generated content conditional text could be "I am preparing to create a video about entrepreneurship and inspiration. I hope to attract comments and attention from interested netizens. Please generate a corresponding forwarding message for me, with a word count of 30 or fewer." Furthermore, the content generation model can generate at least one candidate promotional content based on the input object type and the generated content conditional text.

[0099] In step S230 of some embodiments, one or more most suitable content may be selected from the candidate promotional content as the target promotional content. The candidate promotional content may include content generated based on the current object type and generated content conditional text, or may include historical content already stored in the content library. Furthermore, the system used in the object recommendation method of the present application may tag the target promotional content in the content library, record the selection results of the target object, and update the priority of the target promotional content in the content library, so that it can be weighted in the next output content sorting.

[0100] It should be noted that the starting score of candidate promotional content stored in the content library is 0. Each time the target selects a promotional content, 1 point is added to the selected promotional content. Therefore, the top n promotional content (n is a positive integer) will be prioritized for display next time, ranked from highest to lowest. Newly generated promotional content will also appear at the next ranking position. Since model-generated content may be identical to historical promotional content stored in the content library, after obtaining the ranked content list, duplicate content can be removed from the list and the top ranking content retained to update the ranked content list, improving content selection efficiency.

[0101] It's important to note that each selection by the target audience updates the score weight of each candidate promotional content, resulting in different priorities for the next presentation. Since the content generation model doesn't initially understand the target audience's content preferences, it can continuously update the content generation model based on the labeled content library to improve the match between generated content and the target audience's needs. The order of generated content presentation can also be continuously adjusted to ensure that the generated content better meets the target audience's preferences.

[0102] It should be noted that the selection criteria for selecting one or more most appropriate content from the generated candidate promotional content may include the relevance, attractiveness, expected effect, etc. of the content, and are not specifically limited.

[0103] It should be noted that in addition to marking the target promotion content as a whole, this application can also extract content key information from the target promotion content and mark the content key information to store the marked content key information in the content library, so that the content key information can be combined according to the input object type and the generated content conditional text to improve the accuracy of content generation.

[0104] In the above embodiment, the present application can generate content based on the object type and the generated content condition text, and select more matching target promotion content in combination with the content and content tags already stored in the content library, which can improve the accuracy of content generation and the efficiency of content selection, thereby improving the accuracy of object recommendation.

[0105] It should be noted that in actual applications, the present application can set the content generation model in a functional module of the application. The target object can trigger the generation control (such as "Start Generation") corresponding to the functional module by entering a prompt and content generation requirements in the prompt input box corresponding to the functional module, thereby triggering the subsequent workflow and generating a ranked content list containing multiple candidate promotional content for the target object to select.

[0106] In step S120 of some embodiments, the content interaction data may include content interaction sub-data for multiple content interaction objects. Content interaction sub-data refers to the data generated by each content interaction object after interacting with the target promotional content in the application. For example, for the target promotional content uploaded by the target object in the application, if 10 objects comment on the target promotional content, 3 objects forward the target promotional content, and 4 objects like the target promotional content, and there are no duplicate objects among the objects that comment, forward, or like the target promotional content, then the content interaction data may include content interaction sub-data for 17 objects.

[0107] It should be noted that if the same content interaction object both comments on and forwards the target promotion content, the content interaction sub-data corresponding to the content interaction object includes both comment interaction data and forwarding interaction data.

[0108] In step S130 of some embodiments, the interactive object category is used to indicate the emotional direction of the interaction between the content interactive object and the target promotion content in the application. The interactive object category may include a positive interaction category and a negative interaction category. The positive interaction category is used to characterize that the emotional direction of the content interactive object towards the target promotion content is positive, that is, expressing interest, recognition, or positive emotions, such as liking the content, and comments including positive words such as "great", "strongly agree", "inquiry event details", etc. The negative interaction category is used to characterize that the emotional direction of the content interactive object towards the target promotion content is negative, that is, expressing disinterest, disapproval, or negative emotions, such as comments including negative words such as "dislike" and "disagree".

[0109] See also Figure 3 , Figure 3 This is an optional flowchart of step S130 provided in an embodiment of the present application. In some embodiments, step S130 may specifically include steps S310 to S370:

[0110] Step S310, obtaining content interaction text from content interaction sub-data;

[0111] Step S320, performing text segmentation on the content interaction text to obtain content interaction words;

[0112] Step S330, performing word vectorization on the content interaction words to obtain interaction word vectors;

[0113] Step S340, obtaining a preset word vector of a preset category keyword;

[0114] Step S350, extracting key features from the interactive word vector to obtain an interactive keyword vector;

[0115] Step S360, performing vector similarity calculation on the preset word vector and the interactive keyword vector to obtain word vector similarity;

[0116] Step S370: Determine the category of the interactive object based on the word vector similarity.

[0117] In some embodiments, in step S310, when determining the object interaction category, the determination can be made based on the content interaction text (i.e., the content comments). If the content interaction sub-data does not contain the content interaction text, content operation information can be obtained from the content interaction sub-data. In this case, the content operation information can be forwarded, liked, etc. In this case, the interactive object category can be directly determined to be a positive interaction category, that is, the content interaction object is interested in the target promotion content.

[0118] In step S320 of some embodiments, the extracted content interaction text may be further subjected to word segmentation, which is the process of breaking the text into smaller content interaction words (such as words or phrases). Text segmentation can help the system better understand the constituent elements of the text, making subsequent analysis more effective.

[0119] In step S330 of some embodiments, the segmented content interaction words are further vectorized. Word vectorization is a method of converting words into digital vectors, which enables computers to understand and process text data. Common models include Word2Vec and GloVe, and word vectors can capture the semantic relationships between words.

[0120] In steps S340 to S360 of some embodiments, the present application may obtain preset word vectors for preset category keywords from a predefined vocabulary. Preset category keywords refer to pre-set keywords that represent positive interaction categories and negative categories, and the preset category keywords and corresponding interaction categories are stored in the vocabulary. Furthermore, key features can be extracted from the interactive word vectors, that is, principal component analysis or other feature selection methods can be used to find the most meaningful part for classification or analysis from the higher-dimensional word vectors to obtain the interactive keyword vectors. Then, by calculating the similarity between the interactive keyword vector and the preset word vector of the preset category keyword, the degree of similarity between the two in the semantic space is determined. This is usually quantified using methods such as cosine similarity and Euclidean distance.

[0121] In step S370 of some embodiments, the interaction category of the preset category keyword with the highest word vector similarity is further used as the interaction object category of the content interaction sub-data.

[0122] In the above embodiment, this application analyzes the content interaction text of the content interaction sub-data, extracts keywords and performs vectorization processing, and further analyzes the emotions and preferences of the content interaction objects towards the target promotion content, thereby providing effective data support for content interaction optimization and improving promotion effects.

[0123] In step S140 of some embodiments, in order to determine whether the content interaction object is suitable for being recommended to the target object, further evaluation of the content interaction object is needed. Specifically, candidate object features of the content interaction object and historical interaction data of the content interaction object in the application can be obtained first. The candidate object features can represent basic feature information of the content interaction object, which can include geographical location information, interest preferences, etc. The historical interaction data refers to the interaction history data of the content interaction object with other contents in the application in the past.

[0124] In step S150 of some embodiments, further, a preference matching score of the content interaction object and the target promoted content can be determined based on the historical interaction data, i.e., the matching degree is quantified by calculating a score, and the higher the score is, the better the matching degree is. The preference matching score can be a probability value (i.e., any value in the interval of 0-1) or a score, to reflect the preference matching degree of the content interaction object and the target promoted content.

[0125] Please refer to Figure 4 , Figure 4 is an optional flowchart of step S150 provided by the embodiments of the present application. In some embodiments, step S150 can specifically include steps S410-S440:

[0126] In step S410, associated object data of the content interaction object is obtained from the historical interaction data.

[0127] In step S420, based on the associated object data, a total number of associated objects, a total number of same attribute objects, and a total number of common associated objects of the content interaction object are determined.

[0128] In step S430, based on the content interaction data, an interaction frequency and a main interaction type of the content interaction object and the target object in the application are determined.

[0129] In step S440, based on the total number of associated objects, the total number of same attribute objects, the total number of common associated objects, the interaction frequency, and the main interaction type, a preference matching score of the content interaction object and the target promoted content is determined.

[0130] In step S410 of some embodiments, the associated object data refers to object data associated with the content interaction object in the application, which can include object data followed by the content interaction object, or object data following the content interaction object. Through these data, a network relationship of the content interaction object can be constructed.

[0131] In step S420 of some embodiments, the total number of associated objects is used to represent the number of objects associated with the content interaction object in the application, the total number of same attribute objects is used to represent the number of objects associated with the content interaction object in the application and having the same preset promotion category, and the total number of jointly associated objects is used to represent the number of objects jointly associated with the content interaction object and the target object in the application. For example, in the application, the account of the content interaction object follows 20 accounts in total, of which 10 accounts are of the entrepreneurship category (and 3 accounts are jointly followed by the target object), 5 accounts are of the food category, and 5 accounts are of other categories without fixed categories. At this time, the total number of associated objects of the content interaction object is 10, the total number of same attribute objects is 5 since the preset promotion category is the entrepreneurship category, and the total number of jointly associated objects is 3.

[0132] In step S430 of some embodiments, further, the content interaction sub-data (such as historical interaction records, comments, likes, etc.) can be analyzed to obtain the number of interactions between the content interaction object and the target object within a period of time, and to obtain the main interaction form (such as the most frequently used one of the interaction types such as comments, shares, likes, and browsing times) between the content interaction object and the target object, that is, to obtain the interaction frequency and the main interaction type of the content interaction object and the target object in the application.

[0133] In step S440 of some embodiments, after determining the five dimensions of the total number of associated objects, the total number of same attribute objects, the total number of jointly associated objects, the interaction frequency, and the main interaction type, since the application will set corresponding intervals and scores for the indicators of each dimension in advance, the scores corresponding to the indicators of each dimension can be determined, and thus the preference matching score of the content interaction object and the target promotion content can be determined.

[0134] It should be noted that the application is not limited to only these five dimensions, and can be flexibly adjusted according to actual needs to obtain a more accurate preference matching score.

[0135] In the above embodiments, the application can determine the preference matching score based on the total number of associated objects, the total number of same attribute objects, the total number of jointly associated objects, the interaction frequency, and the main interaction type, which can better understand the behavior characteristics and preferences of the content interaction object and improve the accuracy of determining the preference matching score, thereby improving the accuracy of object recommendation.

[0136] Please refer to Figure 5 , Figure 5 which is an optional flowchart of step S440 provided by the embodiments of the application. In some embodiments, step S440 can specifically include steps S510 to S560:

[0137] In step S510, a first matching score of the content interaction object is determined based on the total number of associated objects.

[0138] Step S520: determining a second matching score of the content interaction object based on the total number of objects with the same attribute;

[0139] Step S530, determining a third matching score of the content interaction object based on the number of commonly associated objects;

[0140] Step S540: determining a fourth matching score of the content interaction object based on the interaction frequency;

[0141] Step S550: determining a fifth matching score of the content interaction object based on the primary interaction type;

[0142] Step S560: Determine a preference matching score between the content interaction object and the target promotion content based on the first matching score, the second matching score, the third matching score, the fourth matching score, and the fifth matching score of the content interaction object.

[0143] In steps S510 to S560 of some embodiments, the indicator of the total number of associated objects can be divided into three intervals, wherein the score corresponding to [0,30) is 5, the score corresponding to [30,80) is 10, and the score corresponding to [80,+∞) is 20. Thus, by determining to which interval the total number of associated objects of the content interaction object belongs, the corresponding first matching score can be determined. The indicator of the total number of objects with the same attributes can be divided into three intervals, wherein the score corresponding to [0,10) is 5, the score corresponding to [10,20) is 10, and the score corresponding to [20,+∞) is 20. Thus, by determining to which interval the total number of objects with the same attributes of the content interaction object belongs, the corresponding second matching score can be determined. Similarly, the third matching score, the fourth matching score, and the fifth matching score can be determined, and these scores can be added up to determine the preference matching score between the content interaction object and the target promotion content.

[0144] In some embodiments, in step S160, after determining the interactive object category, candidate object characteristics, and preference matching score, a predictive model can be used to estimate the likelihood that the content interactive object will be converted into a preset promotion category in the future, i.e., the target conversion score. The target conversion score is used to represent the degree to which the content interactive object is converted into an object that matches the preset promotion category. For example, in a fintech intelligent sales recruitment scenario, if the preset promotion category is insurance sales entrepreneurship, the target conversion score can represent the likelihood that the content interactive object will be converted into an insurance salesperson.

[0145] See also Figure 6 , Figure 6 This is an optional flowchart of step S160 provided in an embodiment of the present application. In some embodiments, step S160 may specifically include steps S610 to S630:

[0146] Step S610, determining interaction strategy data for the content interaction object based on the interaction object category and the content interaction sub-data;

[0147] Step S620, obtaining object feedback data of the content interaction object to the interaction strategy data;

[0148] Step S630: Determine the target conversion score of the content interactive object based on the interactive object category, candidate object characteristics, preference matching score and object feedback data.

[0149] In step S610 of some embodiments, interactive feedback data refers to feedback actions based on the interaction of the content interactive object with the target promotion content. In order to improve the accuracy of the interactive strategy data, the present application will determine the interactive strategy data for the content interactive object based on the interactive object category and the content interactive sub-data. For example, if the interactive object category is a positive interactive category, then the content interactive sub-data can be analyzed. If the content interactive sub-data indicates a desire for further communication to understand the details, then the content interactive object can be automatically followed or specific contact information can be added. If the interactive object category is a negative interactive category, then interactive strategy data may not be generated.

[0150] In some embodiments, steps S620 and S630 further include obtaining object feedback data from the content interaction object regarding the interaction strategy data. This object feedback data represents the feedback provided by the content interaction object following an operation corresponding to the received interaction strategy data, such as, for example, communication records with the target object during subsequent communication. Furthermore, a target conversion score for the content interaction object is determined based on the interaction object category, candidate object characteristics, preference matching score, and object feedback data.

[0151] It should be noted that the target conversion score of the content interactive object is determined based on the interactive object category, candidate object characteristics, preference matching score and object feedback data. Specifically, it can include: scoring and detecting the interactive object category, candidate object characteristics and object feedback data respectively to obtain corresponding scores, and then adding the multiple scores obtained with the preference matching score to obtain the target conversion score.

[0152] In step S170 of some embodiments, after obtaining the target conversion score, the target object may be recommended based on the ranking results of the target conversion score.

[0153] See also Figure 7 , Figure 7 This is an optional flowchart of step S170 provided in an embodiment of the present application. In some embodiments, step S170 may specifically include steps S710 to S730:

[0154] Step S710, performing level matching based on the target conversion score and the preset rating level to obtain a recommended object level of the content interaction object;

[0155] Step S720 , sorting the content interaction objects based on the recommendation object levels and target conversion scores to obtain a recommendation object sequence;

[0156] Step S730 : selecting a target recommended object from the recommended object sequence based on a preset recommendation quantity, and recommending the target recommended object to the target object.

[0157] In step S710 of some embodiments, considering that the number of content interaction objects may be large, the present application may perform level matching on the content interaction objects of the positive interaction category based on the scoring card method, so that the content interaction objects with scores in the scoring interval corresponding to the same scoring level are considered to be in the same group. For example, the preset scoring levels are divided into four categories, namely S, A, B and C, where the scoring interval corresponding to the S scoring level is [0,60), the scoring interval corresponding to the A scoring level is [60,80), the scoring interval corresponding to the B scoring level is [80,90), and the scoring interval corresponding to the C scoring level is [90,100].

[0158] It should be noted that the number of preset scoring levels and the corresponding scoring intervals can be flexibly adjusted according to actual scoring rules and needs, without specific limitations.

[0159] In step S720 of some embodiments, after the recommendation object level is determined, each recommendation object level may also include target conversion scores of multiple content interaction objects, which can be further sorted based on the target conversion scores of the content interaction objects to obtain a recommendation object sequence.

[0160] In step S730 of some embodiments, further, when the target object needs to contact the content interactive object to complete a task corresponding to a preset promotion category, the target recommended object can be selected from the sequence of recommended objects corresponding to the highest preset rating level according to the rating from high to low based on the preset number of recommendations (for example, the top five, top ten, etc.) determined based on the task corresponding to the preset promotion category, and the target recommended object can be recommended to the target object.

[0161] In the above embodiment, the embodiment of the present application ensures that the content interactive object can obtain the most suitable recommended object through the precise level matching and sorting mechanism from the target conversion score to the recommended object to the final recommendation, thereby improving the accuracy and efficiency of the object recommendation, thereby improving the conversion rate and user satisfaction.

[0162] It should be noted that the non-Company's software tools or components appearing in the embodiments of this application are merely examples and do not represent actual use.

[0163] An object recommendation method provided by an embodiment of the present application, by designing an object recommendation method based on the combination of RPA and NLP, can, on the one hand, automatically interact with the associated objects of the target object through RPA, thereby improving the interaction efficiency with key objects; on the other hand, quickly generate the target promotion content required by the target object through a content generation model constructed based on ChatGLM, and predict some data based on NLP to obtain the target conversion score of the content interaction object, so that the target object can give priority to information exchange with the content interaction object with a higher target conversion score, thereby improving the object conversion efficiency. Therefore, compared with the related art that does not consider various factors related to the preset promotion category when selecting objects, the present application can fully consider various factors related to the target promotion content and the content interaction object itself by determining the preference matching score and the target conversion score, thereby improving the accuracy and efficiency of object recommendation.

[0164] See also Figure 8 The present application also provides an object recommendation device that can implement the above-mentioned object recommendation method. The device includes:

[0165] A first acquisition module 810 is configured to acquire content interaction data obtained by a target subject interacting with target promotion content in an application, where the target promotion content includes a preset promotion category;

[0166] An object acquisition module 820 is configured to acquire a content interaction object of a target object based on the content interaction data, wherein the content interaction data includes content interaction sub-data of the content interaction object;

[0167] Category detection module 830, configured to perform object category detection on the content interaction object based on the content interaction sub-data to obtain an interaction object category, where the interaction object category is used to indicate the emotional direction of the interaction between the content interaction object and the target promotional content in the application;

[0168] A second acquisition module 840 is used to obtain candidate object features of the content interaction object and historical interaction data of the content interaction object in the application;

[0169] A score determination module 850 is used to determine a preference matching score between a content interaction object and a target promotion content based on historical interaction data;

[0170] The object conversion module 860 is used to predict object conversion based on the interactive object category, candidate object characteristics, and preference matching score, and obtain a target conversion score for the content interactive object. The target conversion score is used to represent the degree to which the content interactive object is converted into an object that matches the preset promotion category.

[0171] The recommendation module 870 is configured to recommend an object to a target object based on the target conversion score.

[0172] The specific implementation of the object recommendation device is basically the same as the specific embodiment of the above-mentioned object recommendation method, and will not be repeated here.

[0173] The present application also provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the object recommendation method when executing the computer program. The electronic device can be any smart terminal including a tablet computer, an in-vehicle computer, or the like.

[0174] See also Figure 9 , Figure 9 The hardware structure of an electronic device according to another embodiment is shown. The electronic device includes:

[0175] The processor 910 may be implemented as a general-purpose central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is configured to execute relevant programs to implement the technical solutions provided in the embodiments of the present application.

[0176] The memory 920 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 920 can store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 920 and is called by the processor 910 to execute the object recommendation method of the embodiments of this application.

[0177] Input / output interface 930, used to implement information input and output;

[0178] Communication interface 940, used to implement communication interaction between this device and other devices, which can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WiFi, Bluetooth, etc.);

[0179] bus 950 , which transmits information between various components of the device (e.g., processor 910 , memory 920 , input / output interface 930 , and communication interface 940 );

[0180] The processor 910 , the memory 920 , the input / output interface 930 , and the communication interface 940 are connected to each other in communication within the device via a bus 950 .

[0181] An embodiment of the present application further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the object recommendation method described above is implemented.

[0182] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0183] The embodiments described in the embodiments of this application are intended to more clearly illustrate the technical solutions of the embodiments of this application and do not constitute a limitation on the technical solutions provided by the embodiments of this application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0184] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0185] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, i.e., they may be located in one place or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of this embodiment.

[0186] Those skilled in the art will appreciate that all or some of the steps in the methods, systems, and functional modules / units in the devices disclosed above may be implemented as software, firmware, hardware, or appropriate combinations thereof.

[0187] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0188] It should be understood that in this application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0189] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the above-mentioned units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0190] The units described above as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0191] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0192] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes multiple instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: various media that can store programs, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0193] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but are not intended to limit the scope of the present invention. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and essence of the present invention should be within the scope of the present invention.

Claims

1. An object recommendation method, characterized in that: The method comprises: Obtaining content interaction data obtained by a target subject interacting with target promotion content in an application, wherein the target promotion content includes a preset promotion category; Acquire a content interaction object of the target object based on the content interaction data, wherein the content interaction data includes content interaction sub-data of the content interaction object; Performing object category detection on the content interaction object based on the content interaction sub-data to obtain an interaction object category, where the interaction object category is used to indicate an emotional direction of the interaction between the content interaction object and the target promotional content in the application; Obtain candidate object features of the content interactive object and historical interaction data of the content interactive object in the application; Determining a preference matching score between the content interaction object and the target promotion content based on the historical interaction data; Performing object conversion prediction based on the interactive object category, the candidate object characteristics and the preference matching score to obtain a target conversion score for the content interactive object, wherein the target conversion score is used to characterize the degree to which the content interactive object is converted into an object matching the preset promotion category; wherein performing object conversion prediction based on the interactive object category, the candidate object characteristics and the preference matching score to obtain a target conversion score for the content interactive object comprises: determining interactive strategy data for the content interactive object based on the interactive object category and the content interactive sub-data; obtaining object feedback data of the content interactive object on the interactive strategy data; and determining the target conversion score for the content interactive object based on the interactive object category, the candidate object characteristics, the preference matching score and the object feedback data; An object is recommended to the target object based on the target conversion score.

2. The method according to claim 1, characterized in that The recommending an object to the target object based on the target conversion score includes: Performing level matching based on the target conversion score and a preset score level to obtain a recommended object level for the content interaction object; Sort the content interaction objects based on the recommendation object levels and the target conversion scores to obtain a recommendation object sequence; A target recommended object is selected from the recommended object sequence based on a preset recommendation quantity, and the target recommended object is recommended to the target object.

3. The method according to claim 1, characterized in that The determining of the preference matching score between the content interaction object and the target promotion content based on the historical interaction data includes: Acquire the associated object data of the content interaction object from the historical interaction data; Determine the total number of associated objects, the total number of objects with the same attributes, and the total number of commonly associated objects of the content interactive object based on the associated object data, wherein the total number of associated objects is used to represent the number of objects associated with the content interactive object in the application, the total number of objects with the same attributes is used to represent the number of objects with the same preset promotion category associated with the content interactive object in the application, and the total number of commonly associated objects is used to represent the number of objects commonly associated between the content interactive object and the target object in the application; Determining the interaction frequency and main interaction type between the content interaction object and the target object in the application based on the content interaction sub-data; The preference matching score between the content interaction object and the target promotion content is determined based on the total number of associated objects, the total number of objects with the same attribute, the number of commonly associated objects, the interaction frequency, and the main interaction type.

4. The method according to claim 3, characterized in that The determining the preference matching score between the content interaction object and the target promotion content based on the total number of associated objects, the total number of objects with the same attribute, the number of commonly associated objects, the interaction frequency, and the main interaction type includes: Determining a first matching score of the content interaction object based on the total number of associated objects; Determining a second matching score of the content interaction object based on the total number of objects with the same attribute; determining a third matching score of the content interaction object based on the number of commonly associated objects; determining a fourth matching score of the content interaction object based on the interaction frequency; determining a fifth matching score of the content interaction object based on the primary interaction type; The preference matching score between the content interaction object and the target promotion content is determined based on the first matching score, the second matching score, the third matching score, the fourth matching score, and the fifth matching score of the content interaction object.

5. The method according to claim 1, wherein Before obtaining content interaction data obtained by the target object interacting with the target promotion content in the application, the method further includes: Get the object type of the target object and generate content conditional text; Performing content generation on the object type and the content generation condition text based on a preset content generation model to obtain candidate promotion content, wherein the candidate promotion content is stored in a content library of the content generation model; Select target promotion content from the candidate promotion content, and mark the target promotion content in the content library to update the priority of the target promotion content in the content library.

6. The method according to any one of claims 1 to 5, characterized in that The performing object category detection on the content interaction object based on the content interaction sub-data to obtain the interaction object category includes: Obtaining content interaction text from the content interaction sub-data; Performing text segmentation on the content interaction text to obtain content interaction words; Performing word vectorization on the content interaction words to obtain interaction word vectors; Get the preset word vectors of the preset category keywords; Extract key features from the interactive word vector to obtain an interactive keyword vector; Performing vector similarity calculation on the preset word vector and the interactive keyword vector to obtain word vector similarity; The interactive object category is determined based on the word vector similarity.

7. An object recommendation device, characterized in that: The device comprises: A first acquisition module is configured to acquire content interaction data obtained by a target object interacting with target promotion content in an application, wherein the target promotion content includes a preset promotion category; An object acquisition module, configured to acquire a content interaction object of the target object based on the content interaction data, wherein the content interaction data includes content interaction sub-data of the content interaction object; a category detection module, configured to perform object category detection on the content interaction object based on the content interaction sub-data to obtain an interaction object category, wherein the interaction object category is used to indicate an emotional direction of the interaction between the content interaction object and the target promotional content in the application; A second acquisition module is configured to acquire candidate object features of the content interaction object and historical interaction data of the content interaction object in the application; A score determination module, configured to determine a preference matching score between the content interaction object and the target promotion content based on the historical interaction data; An object conversion module is configured to predict object conversion based on the interactive object category, the candidate object characteristics, and the preference matching score, and obtain a target conversion score for the content interactive object, wherein the target conversion score is used to characterize the degree to which the content interactive object is converted into an object matching the preset promotion category; wherein the object conversion prediction based on the interactive object category, the candidate object characteristics, and the preference matching score and obtaining the target conversion score for the content interactive object comprises: determining interactive strategy data for the content interactive object based on the interactive object category and the content interactive sub-data; obtaining object feedback data of the content interactive object on the interactive strategy data; and determining the target conversion score for the content interactive object based on the interactive object category, the candidate object characteristics, the preference matching score, and the object feedback data; A recommendation module is used to recommend an object to the target object based on the target conversion score.

8. An electronic device, characterized in that: The electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the method according to any one of claims 1 to 6 when executing the computer program.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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