523 results about "Recommendation service" patented technology

The invention discloses an intention classification method and device based on vector retrieval and context awareness and a medium, and relates to the technical field of artificial intelligence. The method comprises the steps of extracting business metadata and associating the business metadata with typical problem examples to generate a standardized service description document; encoding the standardized service description document into a high-dimensional semantic vector through a pre-training language model, and constructing a neighbor search index to store the high-dimensional semantic vector; splicing the user identity information and the current question text into an enhanced query statement, and encoding the enhanced query statement into a context-aware dynamic query vector through a semantic model; performing similarity retrieval based on the dynamic query vector to obtain candidate intelligent services, performing business domain filtering, context weighted sorting and dynamic priority rearrangement, and outputting target recommendation services; by collecting interactive behavior data of a target recommendation service, quality scoring is performed on service descriptions and problem examples based on a preset evaluation rule, and the service descriptions and the problem examples of which the quality scores are lower than a quality threshold value are updated.

The invention provides a cross-domain multi-dimensional query feature index selection method, which comprises the following steps of: judging a query field and a query purpose of a user through query subject classification and query intention identification according to historical query behavior data of the user, and obtaining a field tag and an intention tag of the query; a navigation path of knowledge links is optimized, semantic correlation between nodes and small world index algorithm characteristics are comprehensively considered, an optimal knowledge navigation path is recommended for a user, and an optimization strategy is dynamically adjusted according to query historical behaviors and feedback of the user; a knowledge navigation result is presented in a visual interaction mode, a multi-dimensional attribute screening and sorting function is provided, exploration type browsing and deep mining of a user are supported, and personalized knowledge recommendation service is provided according to a query scene and preference of the user.

The invention discloses an image recommendation method based on a graph neural network, and relates to the technical field of recommendation systems, and the method comprises the following steps: constructing model input, constructing a graph neural network model, comparing a learning module, carrying out joint training and optimization, and carrying out recommendation generation and evaluation. According to the method, the relationship embedding matrix in the knowledge graph is considered into the graph neural network model, the splicing vector of the knowledge graph is used as a construction expansion node embedding vector input model, and the complex relationship between the user and the image can be deeply mined through the graph neural network and comparative learning in combination with the interaction graph, so that the user preference is accurately captured, and the user experience is improved. Therefore, the recommendation accuracy is remarkably improved, the multi-dimensional features of the user and the image nodes are expanded, the context sensing technology is combined, the method can provide highly personalized recommendation services for the user, the unique requirements of the user are met, and the user experience is improved.

The invention relates to the technical field of new user data recommendation, in particular to an e-commerce personalized recommendation data system fused with deep learning, which comprises a data acquisition and processing unit, a dynamic monitoring unit, a GAN data enhancement unit and a recommendation service unit. The multi-modal data of users and commodities is comprehensively collected, features are accurately extracted, the dynamic monitoring unit monitors and predicts data changes in real time and generates adjustment signals by applying quantum state space and causal inference network technologies, and the GAN data enhancement unit generates high-quality simulation data based on a deep convolutional generative adversarial network by means of meta-learning, knowledge distillation and other algorithms. And the recommendation service unit is used for training a model according to the enhanced data, so that accurate recommendation is realized, the exposure degree of new commodities is improved, and the accuracy and efficiency of recommendation are improved.

The invention relates to the technical field of artificial intelligence and the field of financial science and technology, and discloses a service content generation method based on emotion recognition, and the method comprises the steps: collecting voice data, extracting voiceprint features, and building a user voiceprint file; extracting Mel frequency cepstrum coefficient features and Mel energy difference coefficient features, and inputting the extracted features into an emotion classification model for emotion classification recognition to obtain an emotion state of the user; the current emotional state is compared with a historical normal emotional state, emotional fluctuation is analyzed, and whether the emotional state of the user is abnormal or not is judged; and when it is detected that the emotional state is abnormal, target service content is determined from the personalized service library in combination with the emotional state and the user voiceprint file. According to the method, the voice features are extracted and the user voiceprint file is established, so that the emotion fluctuation of the user can be accurately analyzed, whether abnormity exists or not is judged, accurate recommendation service is provided in combination with the personalized features and historical behavior data of the user, and the intelligent and personalized degree of vehicle-mounted voice interaction is improved.

The invention relates to the technical field of exhibit recommendation, and particularly discloses an exhibit first push item optimization method combining a user portrait and user positioning, which extracts user positioning data from an indoor positioning system, and determines a target exhibit group based on real-time position and orientation information of a user, and then introducing a deep learning technology to perform semantic analysis on the user portrait data to extract user portrait features, and performing semantic matching analysis on the user portrait features and feature tags of each exhibit in the target exhibit group to determine an exhibit first push item from the target exhibit group. Through the mode, the behavior preference of the user and the real-time physical environment information can be comprehensively considered, a more accurate and personalized exhibit recommendation service is provided for the user, then the visiting experience of visitors is improved, and the resource utilization efficiency of culture display places such as museums and exhibition halls is optimized.

The invention discloses an interest point recommendation method based on a knowledge graph, and relates to the technical field of computers, and the method comprises the following steps: obtaining user sign-in data to obtain user information, respectively dividing according to interest points and time to obtain interest areas and a plurality of time slots, calculating user similarity, and performing spectral clustering according to the user similarity to obtain user groups, and according to the user group, the time slot and the region of interest, constructing a multi-level point-of-interest knowledge graph fused with context information. Preliminarily learning an interest point knowledge graph based on a graph translation model to obtain an embedded vector, and using a gating graph neural network introduced with geographic information for a plurality of sign-in sequences to obtain interest point representation with spatio-temporal characteristics; through the graph attention mechanism model, the sign-in relation with user group features and interest area features is fused, user preferences are spread, possible interest point preferences of a single user are mined and predicted, user requirements can be more effectively met, and a more highly personalized recommendation service is provided.

The invention discloses a cockpit scene recommendation method and device, a storage medium, a program product and a vehicle, and relates to the technical field of artificial intelligence, and the method comprises the steps: carrying out the preprocessing of man-machine conversation text data of a voice burying point log of the vehicle, obtaining the natural language description of a user, carrying out the similarity screening in a preset scene knowledge base, and obtaining a voice burying point log of the vehicle; obtaining corresponding scene knowledge; splicing the user natural language description with the scene knowledge to obtain a scene recommendation prompt word; and performing scene action reasoning on the scene recommendation prompt word through the large model agent, and outputting a scene action execution list corresponding to the scene knowledge. According to the method, man-machine conversation text data is converted into user natural language description, corresponding scene knowledge is screened, scene action reasoning is carried out based on the powerful text understanding and generating capacity of a large model agent, and the intelligent scene recommendation service of the cockpit can be automatically realized according to the voice description of a vehicle owner; and the accuracy of cabin scene recommendation is improved.

Aspects of the disclosure include methods and systems for leveraging a controllable diffusion model for dynamic image search in an image gallery recommendation service. An exemplary method can include displaying an image gallery having a plurality of gallery images and a dynamic image frame. The dynamic image frame can include a generated image and an interactive widget. The method can include receiving a user input in the interactive widget and generating, responsive to receiving the user input, an updated generated image by inputting, into a controllable diffusion model, the user input. The method can include replacing the generated image in the dynamic image frame with the updated generated image.

The invention relates to the technical field of internet finance, in particular to an enterprise credit recommendation method and device based on multi-model integration. The recommendation method comprises the following steps: step 1, data collection; 2, data preprocessing; step 3, feature extraction; 4, model training; 5, credit product matching; and 6, recommending and outputting. According to the invention, by collecting and analyzing the multi-dimensional data of the enterprise, combining the characteristics and requirements of credit products issued by multiple banks, and utilizing machine learning and data mining technologies, personalized and accurate credit product recommendation services are provided for the enterprise. The technology not only improves the accuracy and efficiency of credit product recommendation, but also provides more scientific decision support for banks and financial institutions, optimizes the credit approval process and resource configuration, reduces the credit risk, and improves the overall operation efficiency. And meanwhile, through visual recommendation result display, enterprise users are helped to understand and select credit products more clearly.

The invention relates to the technical field of user data processing, in particular to a user portrait enhancement method based on a dynamic tag, which is applied to a system method for designing data acquisition, data processing, data analysis and data classification of a user information and user portrait data use platform. By analyzing the user behavior in real time and dynamically adjusting the label in the user portrait, the more accurate and comprehensive user portrait can be constructed, and the real-time interest and long-term preference of the user can be captured. Based on the dynamically updated user portraits, the system can provide highly personalized recommendation services, commodity recommendation, content recommendation or service recommendation can be closer to the real demands of the users, and the satisfaction degree and participation degree of the users are improved.

The invention provides a grouping federal recommendation method based on bilateral additive article embedding. A central server constructs a double-layer article embedding characterization structure under a federated learning framework: a client locally maintains user personalized embedding and an article local personalized embedding matrix, and a server generates global group shared article embedding through a dynamic clustering grouping mechanism; superposing global sharing embedding and local personalized embedding by adopting an additive fusion strategy to generate user side personalized article characterization; a progressive course learning scheme is designed, and smooth transition from complete personalization to additive representation is realized by dynamically adjusting a regularization weight coefficient; and a grouping and clustering process is optimized in combination with a knowledge migration strategy, and collaborative knowledge sharing across user groups is promoted. In a client local training stage, a personalized recommendation loss function based on binary cross entropy is constructed, global shared embedding and local embedding parameters are synchronously updated, and a server side updates a global model through grouping federation aggregation. On the premise of protecting user privacy, the problems that in traditional federated recommendation, article characterization is simplified, and personalized perception is insufficient are effectively solved, the accuracy of a recommendation system is remarkably improved, communication overhead is reduced, and the method is suitable for personalized recommendation services of privacy sensitive scenes such as e-commerce and content platforms.

The invention relates to a recommendation method based on a knowledge graph and an attention mechanism, and belongs to the field of personalized recommendation. The method comprises the following steps: defining a knowledge graph perception recommendation problem, and establishing a user and project interaction matrix and a knowledge graph as input; setting seed sets of users and projects in a knowledge graph propagation layer, propagating the seed sets along adjacent entities, and capturing knowledge-based high-order interaction information of the users and the projects; learning a corresponding potential relation representation for each relation through an attention mechanism in the knowledge perception attention embedding layer to obtain a potentially associated entity set; processing the entity set through an aggregator to obtain a corresponding aggregation vector, and predicting a preference score of the user to the item according to the aggregation vector; and designing a loss function to train the process, and performing user preference recommendation after training is completed. According to the method, the potential relationship between the user and the article can be more accurately captured, and more accurate and personalized recommendation service is provided for the user.

The invention relates to a method and a system for realizing personalized recommendation based on multi-feature fusion face recognition, and aims to provide real-time, efficient and accurate recommendation service, the method comprises the following steps: collecting a face image of a target audience in real time through a camera device, and preprocessing the image to extract a face region; running a face recognition algorithm in the edge computing device, and extracting multi-modal features including age, gender, appearance and style features; a comprehensive feature vector is generated by using a Transform model through a feature fusion module, and style classification and appearance classification are realized; according to the method, user feature vectors and a content database are matched on the basis of cosine similarity, personalized recommendation content is generated, the weight of a classification model is dynamically optimized in combination with user feedback, and dynamic behaviors and appearance features of a user are accurately captured through a time sequence analysis module, a dynamic style analysis module and a deep appearance analysis module; and comprehensively generating user feature description by combining vision, voice and character multi-mode information.

The invention discloses a personalized recommendation method driven by user intention recognition, which comprises the following steps of: collecting multi-dimensional information such as browsing records, click behaviors and comment data of a user, and constructing a user behavior data set; preprocessing the collected user behavior data to obtain a session sequence; the current demand of the user is speculated by analyzing the input text and the behavior mode of the user, and the intention of the user is recognized; constructing a user interest preference prediction model according to the identified user intention; and in combination with real-time feedback of the user, the recommendation model is dynamically updated, and the response speed and the individuation degree of the recommendation system are improved. The problem that a traditional recommendation system only depends on static data and does not comprehensively consider real-time feedback of users is solved, self-adaptive ability is injected for personalized recommendation, intention behaviors of different users in multiple scenes can be analyzed and understood, accurate recommendation service is provided, and recommendation accuracy and user satisfaction are improved.

The invention relates to the technical field of computers, and particularly discloses a customer service question and answer statement generation method, a customer service system, equipment and a medium. The method comprises the steps of determining a target question-answering agent for processing a target question based on a planning agent according to a question type corresponding to the target question and function description information corresponding to multiple question-answering agents; processing the target question based on the target question and answer agent, and generating question answer data corresponding to the target question; based on the planning agent, according to the target question, the question answering data corresponding to the target question and a preset question answering strategy, determining whether to execute a preset recommendation service, and when it is determined that the preset recommendation service is executed, generating recommendation data corresponding to the target question based on the recommendation agent; and based on the summary agent, according to the question answer data and the recommendation data, generating a customer service question and answer statement corresponding to the target question. According to the method, the customer service question and answer statement with double targets of question and answer service and recommendation can be generated.

The invention relates to an e-commerce personalized recommendation service method based on genetic fuzzy clustering. Comprising the steps of dividing commodities according to the color richness from low to high, performing analysis to obtain a preference relationship between a user group and the color richness of the commodities, establishing a preference prediction model according to the preference relationship to obtain a preference feature vector of the user group, and obtaining a preference feature vector of the user group according to behavior data of the user group and the preference feature vector. The difference and similarity of different user groups in color richness are analyzed, the user groups are divided into multiple categories of users according to the color richness, the color preference characteristics of the users of each category are obtained, and according to the color preference characteristics and the commodities corresponding to the corresponding color richness, the commodities corresponding to the users of each category are obtained. Generating a commodity recommendation list based on color richness, and dynamically adjusting the recommendation list according to feedback information of each category of users on the recommendation list; the problem that the experience feeling is poor in the process of selecting the required commodity by the user can be effectively solved.

The invention belongs to the technical field of medical treatment and public health, and particularly relates to a hypertension group knowledge recommendation method and system based on a large language model multi-agent. Hypertension knowledge question-answer pair information, medical short video information and user personal health portraits are finely extracted by constructing a hypertension knowledge question-answer pair library; according to the method, video content quality evaluation and matching degree evaluation are performed, and user platform interaction data adjustment is combined, so that personalized and accurate knowledge recommendation services can be provided for hypertension users, the requirements of hypertension patients for health knowledge are met, the accuracy and effectiveness of knowledge recommendation are improved, and self-health management of the patients is facilitated.

The embodiment of the invention discloses a scenic spot route recommendation method and device, electronic equipment and a computer readable medium. A specific embodiment of the method comprises the following steps: acquiring target scenic spot information; constructing a culture gene map about the target scenic area according to the target scenic area information; generating a recommended route and recommended scenic spot information of the target user in the target scenic area according to the target user information and the culture gene map; and sending the recommended route and the recommended scenic spot information to the target user equipment. According to the embodiment, personalized tourism content recommendation of the scenic area is realized, tourists can be helped to experience tourism content recommendation service of the scenic area in the tourism process of the scenic area, the tourism information recommendation accuracy is improved, and the problems of poor tourism experience and low tourism recommendation accuracy in the current tourism process are solved.

A system for providing context-specific item recommendations is provided, for example in support of customer loyalty programs. In examples, a contextual offer recommendation engine utilizes a deep neural network in an epsilon-greedy agent to implement a contextual multi-arm bandit. The contextual multi-arm bandit is used to explore optimal solutions regarding correspondence between offers and customers. The optimal solutions may represent customer-offer combinations which may be published to a campaign manager for display to a customer, e.g., via a retail server. The deep neural network may be continually and adaptively retrained an extended based on observed actions between customers and new or preexisting offers.

The embodiment of the invention provides a customer portrait analysis method and system for financial service, and belongs to the technical field of user portraits. The method comprises the following steps: in response to authorization information of a target customer, collecting business related information of the target customer; preprocessing the business related information, and constructing a customer portrait corresponding to the target customer based on the preprocessed business related information; calling a trained risk assessment model based on the customer portrait to obtain a risk assessment result of the corresponding target customer; and based on the customer portrait and the risk assessment result, obtaining a recommendation service of the target customer, and pushing the recommendation service to a target client. According to the scheme of the invention, the accuracy and response efficiency of financial services are improved, the risk control capability and customer experience of the system are enhanced, and an automatic and intelligent business recommendation process is realized.

The invention relates to the technical field of music recommendation, and discloses a multi-source data intelligent music recommendation method and system, a storage medium and equipment, and the method comprises the steps: collecting multi-source music data, and carrying out the fusion processing of the collected multi-source music data; the method comprises the following steps: identifying and extracting music related entities in data and relationships among the entities, constructing a music ontology and reasoning music knowledge to construct a music knowledge graph; a user portrait is comprehensively constructed by extracting explicit features of a user, constructing a user implicit behavior model, mining a time sequence mode and integrating cross-platform data; user emotion and situation changes are captured in time through context sensing, and a recommendation strategy is adjusted in real time; and carrying out personalized recommendation by combining collaborative filtering, extracted vocal music features and semantic features and a music knowledge graph. Through the method, a multi-source data intelligent music recommendation system is realized, and a highly personalized and context-aware music recommendation service is provided for the user.

The invention discloses an AI-based e-commerce commodity selection method and system. The method comprises the steps of collecting multi-dimensional data of all commodity information uploaded by suppliers on a supply chain platform; preprocessing the multi-dimensional data to obtain preprocessed supplied commodity feature data; acquiring historical behavior data and current demand data of a purchaser on a supply chain platform to predict purchased commodity feature data preferred by the purchaser; and calculating similarity scores of the purchased commodity feature data and the supplied commodity feature data, and generating a personalized supplied commodity push list for the purchaser according to the similarity scores. By using the embodiment of the invention, the commodity information uploaded by the supplier and the historical behavior data of the purchaser can be comprehensively collected and preprocessed, the commodity characteristics capable of reflecting the preference of the purchaser can be effectively extracted, the commodity selection efficiency and accuracy of a supply chain platform are improved, and a better commodity recommendation service is provided for the purchaser.

The invention discloses a science popularization education knowledge recommendation method and system, and relates to the technical field of recommendation service, and the method comprises the following steps: building an emotion recognition mechanism, generating emotion feedback data through an algorithm, and collecting the emotion feedback data of a user in real time; establishing a self-adaptive learning path recommendation mechanism, and dynamically adjusting learning contents and paths according to the collected emotion feedback data of the user; and based on the adjusted learning path, collecting feedback data of the user in the learning process, and evaluating the validity of the learning path and the learning effect of the user. According to the emotion feedback data of the user, personalized learning paths and content recommendation can be dynamically generated, personalized requirements of different learners are met, the learning effect is improved, the emotion feedback data of the user are collected and analyzed in real time through facial expression recognition and voice emotion analysis technologies, and the user experience is improved. The learning content and path are dynamically adjusted according to the emotional state of the user, and more adaptive learning experience is provided.

The invention relates to the technical field of knowledge management systems, and discloses a knowledge full-life-cycle management system and a construction method, and the system comprises a technical platform and multi-modal perception layer, a calculation and arrangement engine layer, a unified API gateway layer, a multi-modal processing module layer, a knowledge graph layer, a cognitive reasoning layer and an application layer. Real-time collection and batch processing of multi-source heterogeneous data are achieved through a standardized SDK / API; spark / Flink is combined with Kubernetes to complete the ETL (Extract Transform Load) and resource scheduling of the multi-modal data; constructing an entity-relationship-attribute knowledge graph through cross-modal feature fusion; intelligent decision support is realized based on rule reasoning, graph calculation and GNN; the application layer provides intelligent questioning and answering, decision support and personalized recommendation services; the technical problems of knowledge islands, sharing barriers, knowledge statics and the like in the prior art are solved.

The embodiment of the invention discloses a commodity information providing model construction method and device, a model training data obtaining method and device, a commodity information providing method and device and AR glasses. And the commodity information providing model is deployed in AR glasses provided by a physical store, and shopping guide service is provided for the customer user in the store through the AR glasses. The method comprises the steps that multiple sets of training data sequentially collected according to the browsing situation of a sample user in a shop in the whole process that the sample user wears AR glasses are obtained, and each set of training data comprises position information of the sample user, identification information of a checked commodity and information of the interest degree of the sample user in the checked commodity; constructing an initial model for providing a shopping guide service; and carrying out model training on the initial model by taking a plurality of groups of training data sequentially collected in the training sample as model input to obtain a commodity information providing model, so that the commodity information providing model has the capability of providing a browsing route recommendation service for the commodities displayed in the physical store.

The invention discloses a personalized cross-domain recommendation method and system based on federal learning, and the method provides a personalized cross-domain recommendation service for a user on the premise that original data of interaction between the user and an article and user parameters are kept locally. The method comprises two stages of federated training: stage 1, intra-domain users cooperatively train a single-domain score prediction model by using a neural collaborative filtering method; in the second stage, overlapping users of the two domains cooperatively train a migration module based on a multi-layer neural network to capture a mapping relation represented by potential user features between the two domains; besides, each layer of network of the cross-domain recommendation model is decomposed into a base vector and a personalized vector which respectively represent common knowledge among different users and unique knowledge of the users, and a local model obtained by final training can provide personalized recommendation services for registered users in a target domain, so that the recommendation efficiency is improved, and the user experience is improved. Meanwhile, the global model obtained through training provides effective initial recommendation for new users in the target domain, and the cold start problem in a recommendation algorithm is effectively relieved.

The invention discloses an automatic construction system for a biological information analysis process, and the system comprises an intention understanding and semantic analysis module which is used for analyzing a natural language text inputted by a user into a structured task description meeting the requirements of a biological information analysis task; the knowledge graph and retrieval module is responsible for constructing a knowledge graph special for the bioinformatics field so as to provide knowledge retrieval and recommendation services; the process generation core module is used for receiving the structured task description, actively associating the knowledge graph with the retrieval module so as to supplement the field large model, and generating a biological information analysis target process language code; and the execution and monitoring module is used for guaranteeing workflow execution, full-life-cycle state monitoring, real-time fault diagnosis and intelligent self-healing decision making of target process language codes. According to the system, the executable analysis process can be directly generated according to the natural language requirement of the user, the tool compatibility and parameter validity are verified through the borrowed knowledge graph before execution, the dependency on the programming ability of the user is greatly reduced, and the process operation reliability is improved.

The invention provides a book recommendation method and system based on multi-source data and a user portrait, and the method comprises the steps: obtaining multi-source user data, and carrying out the cleaning and preprocessing of the multi-source user data, and obtaining effective data; in combination with a clustering analysis algorithm and an association rule mining algorithm, analyzing the effective data, and constructing to obtain a user portrait; generating a book recommendation list matched with the user portrait based on the user portrait in combination with a collaborative filtering algorithm and a content recommendation algorithm; the book recommendation list is pushed to a user terminal, a feedback result is obtained, and the feedback result is used for adjusting algorithm parameters and user portraits. The diversity and accuracy of recommendation results are improved, personalized book recommendation services can be provided based on user portraits, the utilization rate of library resources is remarkably improved, and the reading experience of users is improved.

The invention provides a site selection method and device of a service site, electronic equipment and a computer readable storage medium, and relates to the technical field of computers. The site selection method of the service site comprises the following steps: acquiring address information of all orders in a target area and business sites to which the orders belong; performing clustering processing on all the orders based on the address information and the business points to obtain a plurality of clustering arrays; obtaining associated data of each clustering array, and determining a site selection demand point of the service site from the clustering centers corresponding to the plurality of clustering arrays according to the associated data; the associated data comprises at least one of the number of waybills, the transportation distance, the waybill collection result and the site selection cost. The site selection of the service site can be accurately recommended, and convenience is provided for effective construction of the service site.