1613 results about "Recommender system" patented technology

The invention relates to a multi-modal dynamic optimization educational resource recommendation system and method, and the system comprises the following modules: a multi-modal data collection module integrates video behaviors, answer tracks, physiological signals and other data through edge calculation, and constructs a learning feature map; the student portrait module adopts an LSTM-Attention network in combination with a graph neural network to dynamically model knowledge mastery and learning styles; the resource matching engine realizes multi-objective optimization of knowledge gain, cognitive load and interest matching based on reinforcement learning and knowledge graph analysis; the tag adaptive module dynamically adjusts resource weights through causal inference and comparative learning, the personalized recommendation module generates a dynamic learning path and pushes adaptive resources based on student portraits and real-time behavior data, and the learning progress tracking module monitors a learning state in real time and feeds back the learning state to the resource matching engine to optimize a recommendation strategy in a closed loop mode. The technical defects that resource recommendation of a traditional education platform is rigid and personalized adaptation is lacked are overcome.

The invention relates to the technical field of data processing, and discloses a cross-border e-commerce commodity recommendation system and method based on multi-source data fusion. The system comprises an acquisition module for performing data acquisition to obtain a cross-border unified data warehouse and federal learning cooperation data; the classification module performs text classification processing to obtain a user preference analysis result and an interpretable attention mark; a quantization module carries out quantization processing to obtain a cross-border selection feature matrix; the fusion module carries out weighted fusion processing to obtain a basic comprehensive score and a weight convergence detection result; the evaluation module performs risk evaluation processing to obtain risk probability distribution and cross-border compliance evaluation results; and the sorting module carries out real-time processing through lightweight preprocessing and a flow-type calculation pipeline to obtain a comprehensive score sorting list of cross-border selected products. The problem that a traditional cross-border e-commerce product selection method cannot effectively integrate multi-source heterogeneous data and cannot analyze user feedback text semantic information is solved.

The invention discloses a cockpit active recommendation system and method based on a knowledge graph and semantic reasoning, and relates to the technical field of intelligent cockpits. The system receives natural language voice input of a user, executes voice recognition and semantic analysis, extracts user intention, keywords and slot entities, generates structured semantic information, constructs or calls a knowledge graph structure with semantic relation edges in combination with environment context information, and obtains the knowledge graph structure with the semantic relation edges. Semantic path reasoning is carried out based on the path dependence weight and the semantic similarity, a semantic edge label guided graph attention mechanism is introduced to calculate a path consistency score, a candidate recommendation set is generated, the semantic fitting degree and the path score are fused to sort and output recommendation content, and the graph edge weight and the user portrait are updated based on user feedback. According to the method, semantic understanding precision, recommendation path interpretability and system adaptive capacity are improved, and the method is suitable for personalized voice recommendation, man-machine interaction and scene linkage control tasks in an intelligent cockpit.

The invention discloses an educational resource image intelligent recommendation and multi-scale matching system and method based on machine learning, and relates to the technical field of intelligent education, a permeability evaluation model is constructed by collecting interactive behavior data of students and educational resource images to quantify cognitive stability and knowledge mastery parameters; synchronously performing multi-scale analysis on the image to generate a visual feature, a knowledge association feature and a cognitive guide feature; and determining an optimal recommendation level based on dynamic granularity selection processing, generating an enhanced image with a permeability feedback mark, and interactively generating learning evaluation data through the enhanced image. According to the method, cognitive dynamic evaluation and multi-scale feature matching are fused, so that the technical bottleneck of a traditional recommendation system on image granularity selection and cognitive adaptation is solved, and the educational resource recommendation accuracy and learning efficiency are remarkably improved.

The invention belongs to the technical field of intelligent medical treatment, and particularly relates to a traditional Chinese medicine tongue diagnosis and prescription recommendation system based on multi-modal feature fusion. The system comprises a data preprocessing module used for preprocessing tongue picture image data and text data; the feature extraction module is used for extracting image features and text features from the preprocessed tongue picture image data and text data; the multi-modal feature fusion module is used for effectively fusing the extracted image features and text features by adopting a self-attention mechanism as a core fusion strategy to generate joint features; the multi-task learning module is used for completing parallel learning of a plurality of tasks on the basis of the representation of the joint features; and the model training and optimization module is used for designing a training method, a loss function and an optimization algorithm, so that the targets of disease name judgment, symptom judgment and prescription conditioning recommendation are completed after the multi-task neural network model processes the joint features.

The invention relates to the technical field of live broadcast, and discloses a live broadcast scene perception recommendation system, which is characterized in that a data acquisition module is used for acquiring multi-source data in a live broadcast process in real time; the scene analysis module is connected with the data acquisition module and is used for receiving and integrating the acquired data and analyzing the live broadcast scene; the user portrait construction module is used for tracking a behavior track of a user on a live broadcast platform in real time, and constructing a dynamic user portrait in combination with historical watching data, a collection record, a consumption record and an interactive behavior in current live broadcast; a recommendation strategy generation module generates a personalized recommendation strategy according to the result of the scene analysis module and the user portrait constructed by the user portrait construction module; the recommendation content pushing module pushes the recommendation content generated by the recommendation strategy generation module to the user; according to the method, personalized and precise recommendation is realized, and the watching experience and participation degree of the user are effectively improved.

The invention relates to the field of knowledge graph completion, provides a knowledge graph completion method based on multi-modal visual angle perception and a deep neural network, and aims to solve the problems of weak multi-modal information expression ability, rough fusion mode and insufficient structural reasoning ability in the prior art. According to the method, structure information, text description and visual image information of an entity in a knowledge graph are obtained, structure, text and image modal input is constructed respectively, and a graph neural network, a pre-training language model and a visual encoder are adopted for feature coding; weighted fusion and semantic enhancement of multi-modal features are realized through a visual angle fusion mechanism and hierarchical attention processing; cross-modal contrast learning is introduced to improve modal consistency; and carrying out triple reasoning by using a uniform Transform encoder, and verifying a completion result by scores. According to the method, multi-modal semantics are effectively integrated, the entity representation capability and the triple prediction accuracy are improved, the model robustness is enhanced, and the method is suitable for application scenes such as intelligent question answering and recommendation systems and has remarkable practical value and popularization prospects.

The invention discloses a dialogue recommendation method based on combination of a multi-modal semantic graph and prompt learning, which comprises the following steps of: firstly, coding entities in dialogue history based on a knowledge graph, carrying out feature coding on a product text and an image, and then, constructing a plurality of semantic graphs with different modals, including a text semantic graph, an image semantic graph and a collaborative semantic graph, on the basis, a multi-modal semantic graph is innovatively combined with prompt learning, high-dimensional semantic association is fully mined by using a large language model, multi-modal features and structured semantic relationships between products are effectively modeled, and the multi-modal semantic graph is embedded into the multi-modal semantic graph embedded # imgabs0 # with multiple modal semantic relationships fused therein, so that the multi-modal semantic graph embedded # imgabs0 # and the multi-modal semantic relationships between the products are effectively modeled, and the multi-modal semantic graph embedded # imgabs0 # and the multi-modal semantic relationships between the products are obtained. The user preference is dynamically captured in the multi-round dialogue recommendation scene to comprehensively understand the user demand and generate a more accurate recommendation result, so that the accuracy in the recommendation task is remarkably improved, meanwhile, more natural content conforming to the context can be generated in the dialogue task, and the dialogue generation performance of the dialogue recommendation system is improved.

The invention discloses a personalized learning resource recommendation method and system based on multi-agent collaboration and a dynamic knowledge graph, and the method comprises the steps: constructing the dynamic knowledge graph, enabling nodes to be associated with teaching resources (videos, test questions, teaching plans, PPT and the like), and enabling edges to represent the logic relation between the resources; collaborative decision is made through four layers of agents: a target determination agent generates a learning target based on student historical learning data and a graph node state; the path planning agent plans a learning path in combination with the target and the learner model; the resource screening agent matches personalized resources from the path nodes; the user portrait intelligent agent updates the learner model in real time; and finally, generating a dynamic recommendation result and feeding back the optimized knowledge graph. Through multi-agent hierarchical collaboration and dynamic interaction with the knowledge graph, the problems of cold start, incomplete resource coverage and path stiffness of a traditional recommendation system are solved, and precise and adaptive learning resource recommendation is realized.

The invention provides a self-adaptive recommendation method based on dynamic strategy optimization, and aims to solve the problems of memory bank update lag, retrieval strategy lag and insufficient long-tail article coverage of an existing recommendation system. A time sequence behavior coding module, a dynamic memory storage bank, a strategy control module and a heterogeneous fusion module are designed; and real-time optimized personalized recommendation is realized. Specifically, a time sequence behavior coding module is used for generating a user state vector with time decay, a dynamic memory storage library automatically updates historical records based on a mixed scoring mechanism, and a strategy control module outputs a learnable retrieval number, a weight coefficient and a fusion parameter. The heterogeneous fusion module realizes cross-channel feature enhancement through a dual-channel attention mechanism; according to the method, the problems of preference drift and long-tail recommendation are effectively solved, meanwhile, the online updating efficiency is improved, and more accurate and diversified recommendation results are provided for users.

The invention discloses a label-based user portrait self-adaptive updating method, a medium and equipment, and solves the problems of poor data timeliness and lagging rule updating in a traditional offline batch processing mode through a collaborative mechanism of real-time stream processing and historical batch processing. A dynamic weight adjustment strategy is adopted, real-time user tags generated by real-time behavior flow data and batch user tags generated by historical behavior batch data analysis are subjected to aging weighted fusion, and dynamic changes of user behavior modes are effectively captured; through feedback behavior data of the user and a closed-loop iteration mechanism of the rule base, self-adaptive evolution of the label rule is realized, and the accuracy and the response speed of a recommendation system are remarkably improved.

Systems and methods for providing item recommendations based on dual models with different levels of product data granularity are disclosed. In some embodiments, a disclosed method includes: receiving, from a computing device, a recommendation request for recommending items to a customer; determining, based on the recommendation request, at least one anchor item to be displayed to the customer; obtaining a first machine learning model trained based on a first product data granularity; obtaining a second machine learning model trained based on a second product data granularity; generating, using the first machine learning model and the second machine learning model, a ranked list of recommended items based on the at least one anchor item; and transmitting to the computing device the ranked list of recommended items to be displayed to the customer with the at least one anchor item.

The invention discloses an intelligent personalized learning path recommendation system, and relates to the technical field of learning systems, and the system comprises a multi-source data collection module which is used for synchronizing behavior data of students in a cross-platform learning scene in real time; the cognitive feature analysis engine comprises a style recognition sub-engine and a demand prediction sub-engine to predict the potential learning demand intensity of students for unmastered knowledge points and generate a priority list comprising knowledge gaps; the personalized path generator generates a three-dimensional path plan; the proportion of guided questioning, example demonstration and autonomous exploration is automatically configured according to knowledge difficulty; the dynamic self-adaptive adjustment unit is used for designing a reward function including short-term progress speed and long-term ability growth potential by taking real-time performance of students as a state space and taking path adjustment action as a decision space based on a reinforcement learning framework; and a double-loop feedback mechanism is realized. According to the method, the dimension limitation of traditional learning analysis is broken through, the difficulty of stiffness of a static course template is broken through, and meanwhile, the semantic gap of subject cognition is broken through.

The invention discloses a recommendation method for enhancing semantics and interest perception by using a large language model. The method comprises the following steps: firstly, performing semantic modeling on unstructured text information such as user comments, article description and the like by utilizing the powerful capability of a large language model in semantic comprehension and user preference modeling aspects, so as to improve the deep perception capability of a recommendation system on user interests and article semantic attributes; then, through a semantic feature alignment and discretization strategy, the problem that continuous semantic representation generated by a large language model is incompatible with features of a traditional recommendation system in the aspect of an expression structure is solved; finally, unified modeling of semantic information and traditional recommendation signals is achieved through a recommendation integration mechanism, and recommendation performance and model interpretability are improved.

The invention relates to the field of recommendation systems, and particularly discloses a personalized recommendation method based on multi-modal behavior sequence modeling, which comprises the following steps of: acquiring multi-modal behavior data such as user text, image, time and place, preprocessing, and realizing dynamic fusion of the data by utilizing a multi-modal self-attention mechanism (MMSA) to obtain a multi-modal behavior sequence model; and the interest evolution of the user is accurately captured. An independent RNN module is adopted to model long-term and short-term interests of a user, and the long-term and short-term interests are combined through a self-learning weight coefficient, so that the change of the user interests is reflected more accurately. In addition, by introducing an online learning and incremental learning mechanism, model parameters are dynamically adjusted according to real-time feedback of the user, and it is ensured that a recommendation result can respond to user interest changes in time. According to the method, the defects of an existing recommendation system in the aspects of data fusion, time sequence modeling and real-time adaptability are effectively overcome, recommendation individuation and accuracy are improved, and the real-time updating capacity and scene adaptability of the system are enhanced.

The invention discloses a diagnosis accompanying service-oriented dynamic user portrait generation and personalized recommendation method, particularly relates to the technical field of medical portrait personalized recommendation, and is used for solving the problem of insufficient user demand dynamic adaptability caused by recommendation homogenization and portrait update stiffness due to excessive dependence on historical behavior data in an existing recommendation system. A dynamically evolved user portrait is constructed by fusing time sequence characteristics of a user behavior sequence, time-space fluctuation characteristics of physiological indexes and spatial semantics of a doctor seeing environment, and the limitation that a traditional recommendation system is disjointed from medical logic in a medical scene is broken through through closed-loop optimization of a disease-service mapping rule and real-time feedback, so that the medical scene recommendation system is more accurate. Co-evolution of demand perception and resource scheduling is realized; the recommendation strategy based on dynamic weight fusion and diversity threshold screening gives consideration to accuracy of service matching and exploratory performance of potential requirements, and recommendation reliability and service response efficiency in a complex medical scene are effectively improved.

The invention provides an intelligent service recommendation method based on multi-dimensional scene perception and dynamic portrait modeling. The intelligent service recommendation method comprises the steps that 1, distributed edge computing nodes are deployed, space-time tetrad data operated by a user are collected in real time, user behaviors and labels are stored, and a high-dimensional recommendation database is built; 2, constructing a dynamic portrait engine, and constructing a user long-term behavior pattern library; 3, deploying a real-time streaming and offline double-engine architecture, fusing real-time scene matching and offline portrait prediction, and performing personalized function and user deep demand analysis; step 4, for new users, synchronously calling geofences to obtain regional hot services, and realizing cold start optimization based on meta reinforcement learning in parallel; and 5, establishing an intelligent recommendation closed loop associated with weather characteristics. According to the method, multi-dimensional features such as space-time tetrad data, user tag information and dynamic interest weights are fully utilized, and the real-time performance, interpretability and generalization ability of a recommendation system can be effectively improved.

A recommendation system identifies resale goods corresponding to retail goods being viewed by a user on a retail goods website. Retail goods metadata and images are extracted from the retail goods website using heuristics and LLM-based methods. The extracted retail goods data undergoes ML model-based product category classification, intelligent image cropping, and color detection. Vector embeddings are generated for images and text using ML models and compared to resale goods vector embeddings stored in a vector database. Multiple result sets are retrieved, fused, and re-ranked using LLM-based and preference-aware re-ranking. A data pipeline continuously loads, cleans, and processes resale goods inventory from resale goods websites.

The invention relates to the technical field of health management systems, and discloses a health management scheme recommendation system based on big data analysis. The system comprises a health data acquisition module, a health characteristic quantification module, a health state identification module, a health scheme prediction module and a health parameter coupling module. The health data acquisition module synchronously acquires three types of time sequence data of physiological indexes, health behaviors and environmental exposure of a user and performs timestamp alignment; a health feature quantification module extracts features from the aligned data and generates corresponding feature matrixes and vectors; the health state recognition module classifies the feature data according to a preset rule and generates a health state label set; the health scheme prediction module is combined with a health intervention measure knowledge base to generate an initial scheme set through an association rule mining algorithm; and the health parameter coupling module corrects the intervention intensity parameter of the initial scheme based on the mapping relationship between the human physiological response and the behavior intervention parameter. According to the system, multi-dimensional health data can be integrated, and an accurate personalized health management scheme is generated.

The invention discloses a multi-user sharing-oriented multimedia network video recommendation method, which comprises the following steps: firstly, constructing multi-user characteristics by utilizing collected program information in a multi-user sharing environment, and constructing a leading user label according to the similarity of the program characteristics and the continuity of user watching behaviors, so that separation of multi-user mixed logs is realized; performing periodic multi-user identification prediction of future sessions; secondly, building a user interest mining model based on a time-varying LinUCB algorithm to learn interest changes of a user for each program theme, and enhancing the personalized ability and efficiency of a recommendation system from three aspects of parallel calculation, adaptive control of an exploration coefficient and incremental updating based on LSTM; and finally, establishing an article quality model based on a non-time-varying LinUCB algorithm to further ensure the program quality, and integrating the two algorithms into a final recommendation system model by adopting a cross weighting strategy to form a final program recommendation list. The novelty and accuracy of the recommendation result are ensured.

The invention relates to the technical field of intelligent recommendation, in particular to a clothing matching intelligent recommendation system and method based on multi-modal learning, and the system comprises a clothing feature extraction module, a matching relation modeling module, a cross-modal learning module, a cross-modal learning module, a clothing matching intelligent recommendation module and a clothing matching intelligent recommendation module, the receiving module is used for receiving node representation and edge representation sent by the matching relation modeling module; constructing a cross-modal graph, and taking the clothing visual features and the attribute text features as nodes of different modals; using a cross-modal graph neural network to learn a relationship between different modal nodes; a comparative learning mechanism is adopted to enhance the consistency of different modal features; the personalized recommendation module is in communication connection with the cross-modal learning module and is used for receiving scene constraint conditions and personal preferences input by a user; generating a personalized clothing matching recommendation list; dynamically adjusting the recommendation list according to user feedback; the complementarity of different modal information is utilized, and the method can adapt to different types of clothing data.

The invention discloses an intelligent question bank retrieval and recommendation system based on an artificial intelligence knowledge graph, and relates to the technical field of artificial intelligence education. Comprising a knowledge graph construction module which is used for processing original education data and constructing a neighborhood knowledge graph comprising a hard preposition relation and a soft incidence relation; the user knowledge state graph construction module is used for constructing a user personal knowledge state graph isomorphic to the domain knowledge graph, and dynamically calculating a mastery index of each knowledge node through a deep knowledge tracking model based on user historical answer data; according to the method, by constructing the domain knowledge graph containing the hard preposition relation and the soft incidence relation, discrete knowledge points are organized into the structured network conforming to the cognitive law, so that the system can understand and follow the internal logic between knowledge, and a learning path which is clear in organization, efficient and coherent is generated.

The invention provides a self-adaptive intent perception enhanced graph contrast learning recommendation method, which comprises the following steps of: constructing a user-article bipartite graph, carrying out multi-layer embedded coding on a user and an article by adopting a graph neural network, and obtaining a multi-intention embedded representation of Gaussian distribution through a variational auto-encoder in combination with a multi-intention hypothesis; noise disturbance is adaptively added to intention embedding so as to enhance feature robustness, and the method respectively implements comparative learning of isomorphic and heterogeneous nodes in a node interaction space domain and an intention perception domain, so that the problems of data sparsity and intention entanglement are effectively relieved; and finally, carrying out joint optimization on recommendation task loss, KL divergence loss and double-domain comparison loss, and realizing accurate modeling and recommendation of the personalized preference of the user. Experimental results show that the method is superior to a mainstream recommendation system on a plurality of real data sets, and has strong generalization ability and robustness.

The invention discloses an online curriculum recommendation method and system based on a large language model, and relates to the technical field of curriculum recommendation. The method is based on multi-level analysis of policy texts, the association between dominant changes and hidden skill requirements is accurately extracted, and the one-sidedness of traditional keyword matching is avoided; curriculum features with attenuation weights are generated through cross-modal alignment, recommendation priorities are dynamically adjusted in combination with policy release time and content update frequency, and recommendation deviation caused by curriculum update lag in the policy transition period is solved; an LLM is used to construct a cross-skill domain incidence matrix, and the secondary influence of policy change is automatically deduced; a virtual interaction data generation module is adopted to simulate a typical user behavior path, the stability of a recommendation system is maintained in the absence of real data, and the model iteration period after policy mutation is shortened.

The invention provides an information recommendation method and device based on collaborative sampling of a knowledge graph and an adjacent graph, and relates to the technical field of information recommendation. According to the method, the triple is constructed by obtaining the historical interaction data of the user, and the triple is fused with the project attribute graph to construct the collaborative knowledge graph; performing expression alignment through projection transformation to obtain initial embedding expression; performing multi-hop neighbor sampling through biased random walk to obtain a multi-hop neighbor set; determining a neighbor which is most similar to the embedding representation of the positive sample item as an anchor neighbor; then linear interpolation is carried out, and the embedding representation of the difficult negative sample is synthesized; on the basis of the multi-head graph attention network, training by adopting a positive sample and a synthesized difficult negative sample, and aggregating to obtain an updated embedded representation; and calculating an interaction probability based on the updated embedded representation, and generating a recommended item for the user. According to the method, the problems of user-project interaction sparsity and false negative example interference are effectively solved, and stronger semantic comprehension and generalization ability are provided for a recommendation system.

The invention discloses a subcutaneous injection site detection and recommendation system based on an intelligent algorithm, and aims to improve the safety of long-term subcutaneous injection and the drug absorption efficiency and reduce the risk of related complications such as subcutaneous hemorrhage, subcutaneous induration and subcutaneous fat hyperplasia at the same time. Comprising a subcutaneous tissue detection module for acquiring subcutaneous vascular distribution, fat layer thickness and induration state of a target part through infrared imaging and ultrasonic technologies and generating subcutaneous tissue characteristic data; the data processing module analyzes the feature data by using an image recognition algorithm, and recognizes a blood vessel dense region, induration and a part where fat hyperplasia has occurred; the injection part recommendation module is used for evaluating an analysis result of the data processing module based on a machine learning algorithm in combination with personalized injection historical data of the patient, and generating an optimal injection part recommendation scheme avoiding a risk area; the intelligent imaging and data analysis technology is utilized, the subcutaneous injection part selection accuracy is improved, and the patient comfort and the treatment effect are improved.

In one implementation, a device determines a target level of performance required by a computer network. The device forecasts usage of the computer network. The device runs simulation to determine whether replacing networking equipment in the computer network with new equipment will reduce energy consumption by the computer network and still satisfy the target level of performance, based on the usage of the computer network that was forecast by the device. The device sends, to a user interface, a recommendation to replace the networking equipment with the new equipment based on a result of the simulation.

The invention provides a passenger cabin service intelligent auxiliary system based on a multi-modal interaction technology. The passenger cabin service intelligent auxiliary system comprises a multi-modal information acquisition module, a passenger intention identification module, an intelligent service scheduling module, a multi-modal feedback module and a data management and self-learning module. According to the passenger cabin service intelligent auxiliary system based on the multi-modal interaction technology, the demand identification accuracy is high, the emergency response speed is extremely high, the workload of a steward is greatly reduced, and the service efficiency is greatly improved; health monitoring and early warning accuracy is extremely high, block chain evidence storage guarantees data integrity and traceability, and safety is enhanced; the federated learning-driven recommendation system greatly improves the satisfaction degree of passengers, the holographic projection interaction naturalness score is high, the personalized experience is optimized, various complex scenes such as day and night, bumping and strong light are supported, the system availability is extremely high, and the environmental adaptability is good; and multi-modal deep fusion is realized, and high-precision interaction is still kept under environmental interference.

Aspects of the disclosed technology include computer-implemented systems and methods for conversational recommendation systems, such as conversational chatbots that are configured to process user queries and generate responses. A recommendation system can receive a user query, provide a recommendation response, and solicit feedback from a user in a target domain. The system can display a first set of items and receive inputs indicative of preferences relative to the first set of items. The system can generate preference embeddings in an embedding space of the target domain based at least in part on the preferences and compare the preference embeddings with item embeddings in the embedding space. The system can select content items based at least in part on a distance between the preference embeddings and the item embeddings in the target embedding space and generate data for displaying the selected content items via the user interface.

A recommendation system is described, which identifies engaged fans for an artist and requests input from the engaged fans regarding a collaboration by the artist with at least one different artist. In implementations, engaged fans are identified as having user profiles on a media content platform that satisfy at least one threshold engagement criteria based on consumption of at least one media content item associated with the artist. The recommendation system presents a user interface that includes at least one prompt for feedback that enables engaged fans to recommend how the artist collaborate with others. In some implementations, the user interface includes controls that are selectable to define artist characteristics to feature in a collaboration and the recommendation system is configured to generate a synthesized collaboration by automatically combining different artists' characteristics using a trained machine learning model. Recommendations based on engaged fan feedback are then provided to artists.