427 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 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 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.

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 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.

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 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.

According to the intelligent recommendation method and device based on driving behavior analysis provided by the embodiment of the invention, accurate analysis of user interests is realized by innovatively constructing a multi-dimensional feature fusion mechanism and integrating registration information, video data, driving data and scene perception data. And designing a trajectory prediction model based on a recurrent neural network, and establishing a feature weighted fusion strategy for intelligent matching in combination with a user interest modeling network of an attention mechanism. And an online learning mechanism is introduced, and model parameters are continuously optimized through clicking, staying duration and visit record data, so that dynamic adjustment of personalized recommendation contents is realized. According to the method, the defects of the traditional technology in the aspects of feature extraction, interest modeling, recommendation strategies and the like are effectively overcome, and the recommendation service level and the user experience in the driving scene are remarkably improved.

The invention relates to the technical field of big data processing, in particular to a service recommendation method, system and device, a server and a storage medium. The method comprises the steps that when an incoming call request of a target user is received, a user portrait of the target user is called, and the user portrait comprises historical behavior information, basic information, historical service information and historical business information; the service labels are analyzed based on the user portraits, a correlation sequence is obtained, the correlation sequence comprises the correlation between the target user and each service label, and the correlation represents the probability that the user needs the service corresponding to the service label; determining a recommendation service type for the target user from the correlation sequence, and generating recommendation voice based on the recommendation service type; and then an incoming call request is accessed, and recommended voice is broadcasted for the target user. By adopting the method, the customer service efficiency can be improved.

The invention discloses application of privacy matrix decomposition on medical recommendation service based on a large language model. The system comprises a data acquisition module; a privacy matrix decomposition module; a large language model processing module; a recommendation decision module; the user interface module breaks through the restrictions of regions, time and professional resources through personalized online medical recommendation services, can obtain high-quality medical consultation and health recommendation services for social vulnerable groups such as remote areas, rural areas, old people and patients with mobility difficulties, promotes fair distribution of medical resources, enhances user trust, and improves the user experience. Particularly, due to the application of a privacy matrix decomposition technology, worries of a user on personal health data safety are eliminated, the trust degree of the user on a medical platform is improved, health consultation service popularization is promoted, the overall health management consciousness and the self-health-care ability of the society are improved, medical knowledge and health management concepts are popularized by means of a convenient online platform, and the health management system has a wide application prospect. Misdiagnosis and treatment caused by asymmetry of medical information are reduced, and the health quality of the whole society is improved.

The invention discloses a service recommendation method and device, electronic equipment and a computer program product. The method comprises the steps that archive information and scene information of a to-be-recommended user are collected, the archive information is used for representing the personal condition of the to-be-recommended user, and the scene information is used for representing the environment condition of the to-be-recommended user and user behaviors fed back by the to-be-recommended user; analyzing the archive information by using a preset user portrait model to obtain a to-be-recommended user portrait of the to-be-recommended user; fusing the to-be-recommended user portrait of the to-be-recommended user with the scene information to obtain a fusion feature of the to-be-recommended user; and recommendation services conforming to the fusion features are selected from the multiple preset services, each preset service has a corresponding feature tag, and the recommendation services are the preset services with the feature tags conforming to the fusion features. According to the method and the device, the technical problem that accurate recommendation cannot be carried out aiming at the service required by the user is solved.

The invention belongs to the technical field of computer networks, and provides a method for finding lawyers online. In order to solve the problem that a traditional lawyer finding mode is inaccurate in matching, case description and files are received through a user input module, and preprocessing, semantic understanding and key information extraction are carried out; the dialogue interaction module obtains legal service demand information through multiple rounds of interaction; the law office-lawyer-judgment document database stores lawyer agent historical case information; the lawyer recommendation module combines various methods to calculate similarity scores, screens out judgment documents with good case results, recommends lawyers, and displays information such as lawyer agent fees; and the charging mode management module realizes the unified setting of the charging function. According to the method, various artificial intelligence technologies are comprehensively applied, efficient and accurate lawyer recommendation service is provided for the user, the matching efficiency is improved, and the transparency and reliability of the recommendation process are enhanced.

The invention discloses a commodity recommendation system based on artificial intelligence, and the system comprises a user behavior tracking module which is used for collecting and processing the multi-source behavior data of a user; the commodity feature extraction and fusion module is used for acquiring and analyzing multi-modal information of commodities; the dynamic updating module is used for adjusting a recommendation list in real time according to instant user behaviors and long-term preferences in combination with short-term memory and long-term memory; the situation awareness module takes external factors such as time, place and weather as input; the explanatory enhancement module is used for adding an explanatory layer for a black box model by using an explanatory machine learning model; the personalized sorting optimization module is used for dynamically adjusting the feature weight and improving the click rate and the conversion rate; and the privacy protection module is used for protecting user privacy by adopting a differential privacy technology. According to the commodity recommendation system and method provided by the invention, many problems existing in a traditional recommendation system are overcome, and a more efficient, more intelligent and safer commodity recommendation service is realized.

In order to solve the problems of low resource utilization efficiency and insufficient personalized service in the prior art, the invention discloses an itinerary planning method, system, equipment and medium based on a guided local search algorithm, and relates to the technical field of big data analysis and artificial intelligence, and the method comprises the steps: obtaining and preprocessing scenic spot data and traffic data; filling a playing list based on the scenic spots selected by the tourists, the scenic spot data and the traffic data; and planning a travel for the travel list based on a guided local search algorithm. Multi-source data including scenic spot opening and closing time, recommended travel duration, geographic position, scenic spot labels and the like are integrated, real-time analysis and intelligent decision are performed by applying an advanced artificial intelligence technology, high-quality travel recommendation service is provided for tourists, personalized requirements of the tourists can be met, the utilization efficiency of tourism resources can be improved, and the tourism experience of the tourists can be improved. Efficient sharing and intelligent analysis of tourism information are realized, and powerful support is provided for tourism decision making.

The invention relates to the field of data processing, and provides an insurance product recommendation method and device, computer equipment and a computer readable storage medium, and the method comprises the steps: obtaining an occupational classification standard file and internal occupational information, and generating an oral title of an occupational language and an oral title of an industry according to the occupational classification standard file; supplementing the internal occupational information according to the oral title of the occupational and the oral title of the industry to obtain an internal occupational standard file; dialogue information between the user and the claim settlement personnel is acquired, and the occupation of the user is identified from the dialogue information according to the internal occupation standard file; and according to the occupation of the user, recommending an insurance product to the user. By adopting the embodiment of the invention, the occupation and industry information of the user can be accurately acquired on the premise of ensuring the data security, so that a more accurate insurance product recommendation service is provided.