25results about How to "Realize personalized recommendation" patented technology

The invention discloses an intelligent customer service training method and device of a multi-modal large model and a storage medium, and relates to the technical field of artificial intelligence. The method comprises the following steps: collecting multi-modal data; performing data duplicate checking on the multi-modal data; preprocessing the duplicate checking data; performing data enhancement on the preprocessed data; the method comprises the following steps: constructing an initialized multi-modal large model based on a Transform architecture; training the initialized multi-modal large model according to the enhanced data, and obtaining a target multi-modal large model; acquiring user inquiry information; extracting weight characters in the user inquiry information; inputting the weight characters into the target multi-modal large model to obtain associated information; preprocessing the associated information to generate a feature vector; obtaining a similarity value between the feature vector and the weight character according to a similarity algorithm; ranking the similarity values according to a sequence to obtain a ranking result; extracting a sorting result; determining target associated information based on the target similarity value; and displaying the target associated information.

The invention provides a multi-channel room reservation method and system for a hotel, and the method comprises the steps: obtaining the room reservation historical data of different reservation channels, the room reservation historical data comprises the room price level, price fluctuation and room type inventory data, segmenting the data according to the customer reservation behavior, the check-in duration, and the average room price key attributes of historical orders, and storing the segmented data in a database; obtaining a room type data set; removing abnormal values and missing values in the room type data set, extracting customer age groups, preferred room types, price sensitivity and historical check-in frequency as key features, and performing normalization processing on the key features to form standardized feature vectors for describing demand features and price sensitivity of each subdivided customer group; and in combination with the real-time room state data of the hotel, the room type demand quantity prediction result and the price sensitivity threshold, dynamically shutting down sales of part of room types.

The invention belongs to the technical field of computers, and discloses an extraction system based on a highlight video in the automobile field, and the system comprises a text semantic analysis module which is used for obtaining the semantic information of the whole video. After a video is input, multi-modal information in an image and original information corresponding to the image and voice are extracted by framing the video, and then a text semantic vector is obtained by converting related titles, texts, voice and subtitles in the video; through a general classification model, a general quality scoring model, a general face detection and recognition model and a fine-grained detection model in the automobile field, image semantic vectors of the whole video can be formed based on the characteristics of the automobile field, and finally audio data in the video are obtained, feature vectors of the audio data are extracted and merged with the image vectors, and the video quality is improved. The semantic vector of the video is obtained, and a Cross Attention structure is utilized to enable the semantic vector of the text to guide the semantic features of the video for prediction.

The invention provides an intelligent interaction method, computing equipment, a storage medium and a program product. The method relates to the field of artificial intelligence, and comprises the steps of obtaining an execution condition and a historical behavior path of a first skill after the execution of the first skill is finished; dynamically deciding a second skill associated with the first skill according to the execution condition and the historical behavior path of the first skill by using the powerful thinking and reasoning capabilities of the large model; according to the technical scheme of the invention, the skill (namely the second skill) associated with the previous skill can be customized and recommended for the user according to the execution condition and historical behavior path of the previous skill (namely the first skill) of the user, and the personalized recommendation of the associated skill / ability can be realized, so that the interactive process of thousands of people and thousands of aspects in the real sense can be realized. And the goal of thousands of users' interaction can be achieved, so that the intelligent interaction system is more intelligent.

The invention relates to a heterogeneous hardware recommendation method and device, a medium and electronic equipment. The method comprises the following steps: receiving hardware demand information; for each piece of candidate heterogeneous hardware, target scores corresponding to the candidate heterogeneous hardware in a plurality of preset score dimensions are determined, and the preset score dimensions comprise a hardware capability dimension, a reasoning performance dimension, a training performance dimension and an ecological support dimension; determining a target scoring dimension in the preset scoring dimensions according to the hardware demand information; and determining at least one piece of to-be-recommended first target heterogeneous hardware in the plurality of pieces of candidate heterogeneous hardware according to a target score corresponding to the candidate heterogeneous hardware in the target score dimension, and outputting hardware information of the first target heterogeneous hardware. Therefore, the performance of the heterogeneous hardware in different scoring dimensions and the actual demand of the user are comprehensively considered, and personalized recommendation for the user can be realized.

The invention relates to the technical field of literature retrieval. The invention discloses a literature recommendation method, device and equipment based on a double-tower model and a medium, and the method comprises the steps: obtaining behavior data of a target user; according to the behavior data of the target user, extracting a behavior preference feature vector of the target user, and calculating a deep representation vector of the target user based on a double-tower model; obtaining a deep representation vector of the candidate literature based on a preset vector database, and calculating the similarity between the deep representation vector of the target user and the deep representation vector of the candidate literature; and generating a literature recommendation list of the target user according to the similarity. According to the method, the semantic features and the interest preference features of the user are obtained by deeply fusing the historical behavior data of the user, a literal matching category is exceeded, user requirements can be more accurately matched, and personalized recommendation is realized. And meanwhile, by utilizing the parallel processing capability of the double-tower model, the response speed of the recommendation system can be increased, and the recommendation efficiency is improved.

The invention relates to the technical field of private domain traffic, in particular to a private domain traffic guiding method and system based on collaborative filtering, and the method comprises the following steps: S1, collecting user behavior data: collecting the behavior data of a user in a private domain platform in real time through client equipment; s2, constructing a user portrait: extracting the interest preference of the user, generating a user interest label, and forming the user portrait; s3, interest extension and cross-domain recommendation: constructing a relation graph between the user and the content, identifying association between the user interest node and the cross-domain content, calculating the edge weight between the user and the content node, and extending the interest boundary of the user in the private domain traffic; and S4, recommended content pushing: pushing cross-domain recommended content to the user, and meanwhile, optimizing the display sequence of the content in combination with the situation information of the user. According to the invention, the diversity and personalization of the recommended content can be enhanced, and the user experience and the diversity of the recommendation effect are further improved.

The embodiment of the invention provides a training course pushing method and device, computer equipment and a storage medium, relates to the technical field of intelligent recommendation, and is suitable for the field of financial science and technology. The method comprises the following steps: obtaining object department information, object post information and object level information, and screening selected post development route information from candidate post development route information according to the object department information, the object post information and the object level information; in response to a route selection operation of the target object on the selected post development route information, screening out target post development route information from the selected post development route information; screening out preliminary training course data from the candidate training course data according to the target post development route information, the object attribute parameters and the training mode demand information; acquiring preliminary learning progress information of the target object based on the preliminary training course data; and adjusting the preliminary training course data according to the preliminary learning progress information to obtain target training course data, and pushing the target training course data to the target object. According to the embodiment of the invention, personalized course pushing can be realized.

The invention relates to a video processing method and device, computer equipment and a storage medium. The method comprises the following steps: identifying an advertisement insertion position in a currently played video, namely identifying a to-be-implanted pixel area, and recommending preset advertisement content conforming to advertisement content preference corresponding to a target account number to the target account number according to an operation record of the target account number for historical advertisement content, and the recommended preset advertisement content is seamlessly implanted into the to-be-implanted pixel region, so that dynamic implantation and personalized recommendation of the advertisement content are realized, and the problem of how to realize personalized and targeted advertisement content delivery for different audiences is solved.

The invention relates to a display device and a personalized recommendation reason generation method. The display apparatus includes: a display configured to display a user interface; an audio output device configured to output sound; the controller is configured to display a search result page in response to a multimedia content search operation triggered by a target user; in the process that the target user carries out multimedia content search for the search result page, target multimedia content is selected; a personalized recommendation reason of the target multimedia content is obtained, and the personalized recommendation reason is generated based on the user portrait data of the target user and the media asset data of the target multimedia content; and controlling at least one of the display and the audio output device, and displaying the personalized recommendation reason. By adopting the display equipment, the conversion rate of a multimedia content search function can be improved.

The present invention provides an intention-guided push method and system based on geographic location information and multimedia interaction, by constructing an interest model based on geographic fence technology and deep neural network; generating user activity intention prediction results based on the interest model, using Bayesian network and time series prediction algorithm based on the user activity intention prediction results to obtain behavior rhythm analysis results, based on determining the best push timing of the behavior rhythm analysis results, using graph theory algorithm to construct a user and location relationship map to generate a time sensitivity optimization plan; based on the time sensitivity optimization plan, generating customized content, using sentiment analysis method, collaborative filtering algorithm, autoencoder and multi-head attention mechanism to process the customized content to obtain final content, adjusting the final content through an instant feedback mechanism, and generating a multimedia push package; the present invention improves the performance and service quality of the intelligent push system, and provides users with a more accurate and personalized recommendation experience.

The present invention provides a user personalized recommendation method based on multimodal data, and relates to the field of network recommendation technology. After obtaining the historical behavior records and image logs that the user allows to be collected, the method extracts all the objects involved therein, as well as the features of each object to be recommended, and maps them to the same multidimensional space, so as to integrate the multimodal data, and then uses the reinforcement learning model as the recommendation system agent, trains the agent through the collected user records, and uses the trained recommendation agent to make recommendations, thereby realizing user personalized recommendations for multimodal data. The user historical behavior records, image logs, and the set of objects to be recommended in the method proposed by the present invention can all contain objects in multiple fields (such as text, pictures, videos, etc.), and blur the distinction between multimodalities through the method of post-feature extraction integration, thereby solving the problem that a traditional recommendation system can only be applied to a single field for recommendation.

This application relates to a method and system for recommending urban park locations based on crowd activity preferences. The method includes: responding to a park location recommendation instruction by detecting user-input information; and obtaining a recommendation result based on a knowledge graph and a trained park location recommendation model, whereby the recommendation result includes at least one location. The knowledge graph is constructed based on a park location feature dataset and a crowd activity preference dataset; the park location recommendation model is trained using a deep learning model based on the knowledge graph; and the park location feature dataset includes multi-dimensional sensory environment data and spatial structure data of all locations within the park. This method enables personalized recommendations for park locations.

The invention relates to a method for dynamically generating a mobile banking activity set page, which comprises the following steps of: constructing a tree structure in which a root node represents an activity set page and a first-level node represents a service category, and dynamically generating child nodes according to marketing activity data, a DIEN model comprising an embedded layer, an interest extraction layer, an interest evolution layer and a prediction layer is used to predict the interestingness of the user to different marketing activities, and user interest prediction is realized; according to the method, the user geographic position, preference setting and interest prediction value are integrated, child node weights are calculated, a descending sorting algorithm is used for sorting the child node weights, activity set pages are dynamically arranged according to the sorting result, and personalized recommendation and efficient marketing are achieved. Through modular design and real-time dynamic updating, the system efficiency and the user experience are improved.

The invention discloses a sequence recommendation method and device based on time feature perception and a storage medium, and relates to the technical field of data processing. The method comprises the following steps: acquiring historical interaction data of a user and a group of items within a preset historical time, and performing context analysis on the historical interaction data to obtain a historical interaction sequence and a timestamp sequence; obtaining a first group of time features, a second group of time features and a third group of time features; obtaining a short-term preference score; obtaining a long-term preference score; generating a comprehensive preference score; determining a plurality of interaction probabilities of the group of items based on the comprehensive preference scores; and sorting the plurality of interaction probabilities according to a sequence from large to small, and taking the item corresponding to the interaction probability ranked in the foremost column as a recommendation result. The technical problems that in the prior art, deviation exists between captured user preferences and current actual preferences, and recommendation reliability is low are solved, and the technical effects that the user preferences are accurately grasped, and recommendation accuracy is improved are achieved.

The invention relates to the field of artificial intelligence and industrial control safety, in particular to a public test task allocation technology based on a recommendation algorithm. The technology aims to efficiently distribute the crowd test tasks to suitable testers or test teams by combining user historical records, user features and task features, and an efficient, accurate and fair crowd test task distribution system is constructed by combining the advantages of various recommendation algorithms. Firstly, collected task data and user data are converted into vector representation by using an NLP technology, then a recommendation algorithm based on attention, an MLP and an LSTM structure is constructed in combination with task features, user features and historical records for crowd test task allocation, and the crowd test task allocation efficiency is improved by adjusting hyper-parameters of a model and a structure training model. And finally, generating a personalized task allocation list for each user, collecting and recording feedback and results of the users on the allocated tasks, and updating historical records and features of the users for decision making during next task allocation.

The embodiment of the invention provides an object recommendation method and device, equipment, a storage medium and a program product, and relates to the field of big data. The method comprises the steps of judging whether an identifier of a target first object exists in an identifier library of the first object or not; if the identifier of the target first object does not exist in the identifier library of the first object, generating a target first object feature vector of the target first object according to the attribute information of the target first object based on a graph convolutional neural network; calculating a first similarity between the target first object feature vector and each first object feature in the identification library of the first object; and determining an identifier of a second object corresponding to the first object feature of which the first similarity is greater than a first preset similarity as an identifier of a to-be-recommended second object corresponding to the target first object. According to the method, the technical effect of improving the personalized recommendation accuracy is achieved.

The embodiment of the invention provides a recommendation method and system and a storage medium. The recommendation system obtains interaction data generated when a target user uses a first group of functions of a service platform, and extracts portrait features of the target user from the interaction data. The recommendation system generates a guide instruction based on the portrait features, and inputs the guide instruction into a first large language model to guide the first large language model to perform reasoning based on the portrait features and determine a target function meeting the demand of the target user, function description information of all functions in a second group of functions of the service platform is injected into the first large language model, and the first large language model is configured to refer to the function description information of the second group of functions in the reasoning process, and the functions meeting the requirements of the target user are determined in the second group of functions. And a recommendation system recommends the target function to the target user through the service platform.

This invention discloses a method for recommending official document approval opinions based on semantic matching and role awareness, comprising the following steps: S1. Constructing a historical document vector database and a structured metadata database; S2. Obtaining documents to be approved and performing feature extraction and preliminary screening; S3. Performing text block-level similarity retrieval on the documents and aggregating them; S4. Calculating job matching scores based on job information; S5. Calculating the final recommendation score and sorting the documents in descending order based on the final recommendation score, outputting the top 5 with the highest scores as the recommended results. This invention improves recommendation accuracy by using a pre-trained semantic embedding model to understand the deep semantics of document content. Furthermore, through job matching score calculation and approval node position alignment mechanisms, it deeply binds the recommended approval opinions to specific approval positions and approval process positions, achieving personalized recommendations.

The invention discloses a data recommendation method and device, electronic equipment and a computer readable medium, and relates to the technical field of computers.The method comprises the steps that user context data and user history browsing item data are obtained according to a received data recommendation request; based on the historical browsing article data of the user, obtaining a pooling vector through a pooling network, and according to the historical browsing article data of the user and the to-be-recommended article data, obtaining a deep interest network representation vector through a deep interest network; determining a first mapping matrix and a second mapping matrix based on the preset representation dimension and the preset real number space configuration data, and further determining a weighted proportionality coefficient based on the user context data; determining a target interest vector according to the weighted proportionality coefficient, the pooling vector and the deep interest network representation vector; and based on the target interest vector, determining target recommended article data in the to-be-recommended article data. The performance of the recommendation system is improved, and personalized recommendation is realized.

The present invention discloses a legal knowledge sharing platform for document reading, which relates to the field of search engines. The legal knowledge sharing platform for document reading includes: a data acquisition module, a historical data processing module, an analysis module, and an output module; the data acquisition module is used to acquire historical data and document content information data to be analyzed; the historical data processing module is used to establish a historical legal knowledge database based on the acquired historical data; the analysis module is used to analyze and match the document content information data to be analyzed based on the historical legal knowledge database. The present invention can improve the accuracy of keyword matching through the establishment and update of the historical legal knowledge database, the historical keyword extraction unit can go deep into the historical document content and extract keyword information, and the historical legal knowledge database update unit ensures that the legal knowledge in the database is kept up to date, thereby increasing the accuracy and matching effect of the analysis module in analyzing the document content.

The invention discloses a knowledge recommendation method and device, a storage medium and electronic equipment. The method comprises the following steps: based on a pre-acquired initial situation feature data set, filtering the pre-acquired initial knowledge feature data set by adopting a preset situation model to obtain a first knowledge data set; based on a pre-acquired user feature data set, filtering the first knowledge data set by adopting a preset user behavior recognition model to obtain a second knowledge data set; sorting the knowledge data in the second knowledge data set by adopting a target sorting model based on the second knowledge data set to obtain a knowledge queue; and screening the knowledge data in the knowledge queue based on the knowledge queue and a preset knowledge quantity threshold to obtain target knowledge data for the knowledge recommendation service. According to the method, the knowledge is gradually screened by adopting the preset situation model and the preset user behavior model, so that personalized recommendation of the task situation information is realized, and the utilization efficiency of enterprise knowledge is improved.

The invention relates to a knowledge recommendation method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring a knowledge attribute data set of each knowledge, a target historical behavior data set of a target user and a target user attribute data set; screening each knowledge based on the target historical behavior data set to obtain each initial target knowledge, and constructing each knowledge user attribute data set according to the knowledge attribute data set of each initial target knowledge and the target user attribute data set; and according to the knowledge recommendation model and the knowledge user attribute data sets, determining the integrating degree of each piece of initial target knowledge and the target user, and according to each integrating degree, determining target recommendation knowledge in each piece of initial target knowledge. By adopting the method, the accuracy of the knowledge recommendation method can be improved.

The present invention proposes an AI-based intelligent management method and system for electronic files. The method belongs to the field of information technology. The method includes: extracting multi-level features of file content based on convolutional neural networks and recursive neural networks, and training an adaptive classification model based on the extracted features; constructing user portraits based on the user's historical operation records and behavior habits based on deep neural networks, introducing reinforcement learning mechanisms, and dynamically adjusting recommendation strategies based on user portraits and current contexts. Through convolutional neural networks and recursive neural networks, multi-level features can be extracted from multimodal files such as text, images, and audio, fully mining the intrinsic information of the files, and improving the accuracy of file classification.

The invention relates to the technical field of virtual reality, in particular to a 3D video conference system and method based on the virtual reality technology. The system comprises a head-mounted display device and a client, the head-mounted display device is assembled and connected with the client, the client is in communication connection with a server, the client is composed of a user management module, a background system management module and a communication system module, the head-mounted display device is connected with the user management module through wireless communication, and the user management module is arranged in a conference room. And the user management module is used for personalized recommendation and management. Through the cooperation between the head display device and the client and the implementation of the whole method, the functions of personalized recommendation and management, user immersive experience improvement, intelligent scheduling and resource allocation, high-precision 3D modeling and real-time rendering, ultra-low delay communication technology, multi-modal interaction technology and the like are realized, the conference efficiency and convenience are improved, and the user experience is improved. And the immersive experience and convenient operation of the user are improved.