2528 results about "Recommendation model" patented technology

The invention discloses a personalized learning recommendation method based on a personalized knowledge graph. The method comprises the steps that the knowledge graph is constructed, and a learner fills in learning style self-description and learning target expected content; the current learning condition of the learner is subjected to primary research evaluation through the enrollment test module; an initial learner portrait model is established, a learner enters a database for learning, and a learning evaluation module performs staged evaluation on the learning condition of the learner; updating a learner portrait model, and generating a learning path and learning resources adaptive to the learner by using a knowledge graph technology and a reinforcement learning DQN network; updating the learning path database; the new learning path is recommended to the learner; according to the method, the learning data of the students are analyzed to determine the personal basic conditions of the students, the learner portrait model is constructed, the knowledge graph technology and the learning recommendation algorithm are utilized to adapt the personalized learning features to the learning knowledge element sequence, and a personalized learning push path recommendation mode is constructed to assist the students in improving the learning effect.

The invention relates to the technical field of educational resource recommendation, in particular to an educational resource recommendation method and system based on artificial intelligence. The method comprises the steps of firstly constructing an educational resource engine, then generating a preliminary user portrait by collecting basic information of a user, dynamically updating the user portrait in combination with personalized demands of the user, then performing data interaction with the educational resource engine based on a data reliability transmission method, and calling educational resource data. The method comprises the following steps of: selecting a user, performing weighted fusion on recommended contents by adopting a collaborative filtering and content-based recommendation mode, providing personalized learning resources for the user, generating a learning path plan for the user, monitoring a learning behavior of the user in a user learning process by the system, and optimizing a recommendation model in combination with user feedback. Through personalized recommendation, intelligent learning path planning, resource matching optimization, data reliability transmission and adaptive optimization, an accurate, efficient, adaptive and reliable educational resource recommendation effect is realized.

The invention discloses a personalized learning path recommendation system based on artificial intelligence, and relates to the technical field of path recommendation, firstly, the system collects multi-dimensional feature data of a learner, and constructs a personalized feature vector; secondly, in combination with knowledge graph modeling and graph neural network technologies, deeply mining explicit and implicit knowledge point association; then, predicting an optimal learning path by using a sequence recommendation model, and ensuring reasonable sorting of knowledge points; in the learning process, the system combines real-time interaction data, dynamically adjusts a learning path, and continuously optimizes a recommendation strategy through an adaptive optimization algorithm; and finally, based on the learning result and the behavior data, evaluating the effectiveness of the learning path, and updating the knowledge point weight and recommendation strategy through a feedback mechanism, thereby realizing intelligent and self-adaptive personalized learning recommendation, the accuracy and adaptability of learning path recommendation can be effectively improved, learners are helped to master knowledge more efficiently, and the learning recommendation efficiency is improved. And the learning experience and effect are improved.

The invention discloses a multi-modal visual arrangement recommendation method, belongs to the technical field of artificial intelligence and data visualization crossing, and realizes visual arrangement recommendation based on multi-modal input analysis, a dynamic mixed recommendation model and an intelligent optimization algorithm. Comprising the following steps: multi-modal intention analysis: realizing intelligent analysis of multi-modal input through combined use of a base model and a fine tuning model, realizing high-precision intention classification in combination with a pre-training language model and a domain adaptation fine tuning technology, and triggering dynamic prompt word recommendation; performing intelligent layout generation: performing global optimization of component space allocation by adopting a genetic algorithm, performing business rule adaptation by combining a constraint solver, and modeling an interaction relationship between components by utilizing a graph neural network; and dynamic mixed recommendation: constructing a three-level recommendation architecture including collaborative filtering, content matching and reinforcement learning. According to the method, a closed-loop recommendation process of user intention-intelligent recommendation-feedback optimization is realized, and the intelligent level of visual arrangement and the user experience are remarkably improved.

The invention discloses a teaching data management method and system based on artificial intelligence, and the method comprises the steps: obtaining a standardized time series data stream according to a heterogeneous data stream generated by a multi-source teaching platform in real time; based on the standardized time sequence data stream, performing classified encryption on the teaching data through a dynamic hierarchical storage strategy based on attribute-based encryption to obtain a security hierarchical storage topological structure; according to a user query request and a teaching scene label, extracting a target data set from the security hierarchical storage topological structure to obtain an enhanced multi-modal teaching data set; based on the enhanced multi-modal teaching data set, generating an interpretable teaching mode graph through a dynamic sub-graph evolution algorithm; and according to the teaching mode map and the real-time teaching feedback data, generating a personalized teaching recommendation strategy through a course-learner dual-channel adaptive recommendation model. According to the embodiment of the invention, the utilization efficiency of teaching resources can be improved, and personalized and intelligent teaching recommendation and decision can be realized.

The embodiment of the invention provides a smart store data processing method and device based on multi-dimensional data analysis, which innovatively realizes alignment and mapping of spatial-temporal feature sequences by collecting sensor, intelligent terminal and social media data. And performing multi-dimensional data fusion based on the feature weight coefficient, and constructing a commodity recommendation model by using a convolutional neural network and a recurrent neural network. The system continuously optimizes the model performance through incremental learning, and realizes personalized commodity recommendation and store operation trend analysis. And meanwhile, a marketing decision optimization model is introduced, intelligent evaluation and dynamic optimization are performed on a marketing strategy, and the model is continuously updated through feedback data. According to the method, the limitation of traditional single data analysis is broken through, and a comprehensive data processing solution is provided for precision marketing of the smart store.

The invention discloses a customizable AI agent management platform for enterprise demands and a management method thereof, and relates to the technical field of artificial intelligence, and the platform comprises a demand acquisition module, a demand analysis module, an application scene decomposition module, an AI agent deployment module and a monitoring feedback module. Collecting enterprise demand description data for preprocessing, extracting demand elements, converting the demand elements into execution tasks, and constructing an execution task sequence; decomposing the execution task sequence based on the application scene, and constructing a sub-task execution chain adaptive to the demand application scene; constructing an agent recommendation model, selecting a model from the pre-training model library according to the subtask execution chain, and configuring an AI Agent for deploying an application scene; and evaluating the operation effect according to the execution result of the AI Agent, and carrying out optimization and improvement. The AI Agent is flexibly configured according to enterprise requirements, multi-scene application is supported, the threshold of using the AI technology by an enterprise is lowered, and the application effect of the AI technology in actual business is improved.

The invention discloses a personalized learning recommendation method for a learner. The method comprises the steps that the learner completes learning style evaluation and learning target setting of the learner; the current learning condition of the learner is subjected to primary research evaluation through the enrollment test module; an initial learner portrait model is established, a learner enters a database for learning, and a learning evaluation module performs staged evaluation on the learning condition of the learner; updating a learner portrait model, and generating a learning path and learning resources adaptive to the learner by using a knowledge graph technology and a reinforcement learning DQN network; updating the learning path database; the new learning path is recommended to the learner; according to the method, the learning data of students in the platform learning process are analyzed to determine the personal basic condition of the students, the learner portrait model is constructed, the knowledge graph technology and the learning recommendation algorithm are utilized to adapt personalized learning features to the learning knowledge element sequence, a personalized learning push path recommendation mode is constructed, and the learning recommendation efficiency is improved. And the learning effect can be improved.

The invention discloses a concept recommendation method based on large language model structure learning and knowledge representation, and belongs to the field of concept recommendation. The method comprises the following steps: firstly, acquiring learning data and a concept knowledge graph of a student account in a learning process, and preprocessing the learning data and the concept knowledge graph; performing text coding on an enhanced text generated by the large language model by using a pre-training language model to obtain a final hidden vector of each concept, further constructing an enhanced concept knowledge graph, and pre-training a GraphGPS concept encoder based on the enhanced concept knowledge graph and a double-view cross graph contrast learning method; synthesizing education data by a TabDDPM model to pre-train a deep knowledge tracking model; and splicing the concept representation of question embedding, answer embedding and structure perception and the student knowledge state representation to form a final representation, further obtaining a coding sequence, pre-training a sequence coding model and finely adjusting a concept recommendation matrix, and finally forming a concept recommendation model for performing concept recommendation.

The emergency decision support method based on cross-domain knowledge fusion comprises the steps of building an emergency semantic model, building an emergency response graph, building an emergency decision auxiliary model based on the graph and the like, a full-cycle emergency disposal knowledge graph oriented to multiple disaster types is provided, and various emergency disposal knowledge can be effectively integrated and stored. Meanwhile, an emergency decision-making auxiliary model is constructed on the downstream based on the atlas, disaster grade division, multi-hop path query analysis and decision-making recommendation can be carried out according to real-time multi-dimensional disaster data, and the cross-domain text retrieval efficiency is greatly improved; meanwhile, a plan chain recommendation model based on an event chain is provided, disaster chain prediction can be carried out based on rules, refined plan pushing can be given, and a vulnerable community is helped to make emergency risk avoiding and planning in advance in a complex scene.

In view of the need for a conversational recommender system (CRS) in guiding purchasing processes of complex items, embodiments described herein provide a CRS system that creates a realistic purchase scenario and agent evaluation for fulfilling the recommendation objective. Specifically, the CRS system utilizes existing buying guides as a knowledge source for the recommendation model.

Disclosed is a cloud-native application programming interface (API) recommendation method fusing data augmentation and contrastive learning. Service information is included on the basis of a service information double-graph structure, and a mutual attention mechanism is designed to compute an importance degree of each layer of information. A data optimization method for sequence information based on functional similarity and a computation method for similarity between services based on two parts of information are provided; on this basis, data of a service invocation sequence is augmented with the idea of contrastive learning to form an augmented sequence pair; a computational contrastive loss function is combined with a pair-wise recommendation loss function to optimize an overall model, thereby improving the effect of a service recommendation model; and according to a feature embedding representation result of a service, pair-wise recommendation scores are computed to complete service recommendation.

The invention provides a sequence recommendation method based on multi-modal feature fusion, and relates to the technical field of personalized recommendation. The method comprises the following steps: firstly, extracting multi-modal features according to different modal interaction records of a user; different feature extraction methods are adopted for different modes; then carrying out multi-modal feature fusion and learning by adopting a multi-modal fusion module; constructing a multi-modal fusion sequence recommendation model to perform sequence recommendation; according to the method, by means of an attention mask mechanism, each mode can pay attention to information in the own mode, information interaction between the modes can also be achieved, and rich original information in text and image features is effectively reserved. According to the method, the problem that a traditional multi-modal recommendation model is disordered during feature fusion is solved. Meanwhile, the situation that in the prior art, it is difficult to balance ID feature learning and multi-modal feature learning is improved, and the recommendation ability of the model in the face of long-tail projects and cold start scenes is enhanced.

The invention relates to the technical field of medical care, in particular to an intelligent query method for a nursing knowledge base, which comprises the following steps of: performing multi-form standardized processing on a nursing problem input by a user, deeply analyzing a query request by utilizing a semantic understanding model, and obtaining an intelligent query result based on an inference algorithm; the method comprises the following steps: automatically selecting related literatures, clinical data and nursing experience data from a nursing knowledge base, generating a preliminary nursing scheme candidate set in combination with a nursing professional knowledge graph, and performing personalized screening and optimization on the scheme by using an intelligent recommendation model in combination with the current illness state, treatment progress and historical nursing data of a patient; the intelligent recommendation model is self-optimized through user feedback, and the nursing scheme recommendation precision is continuously improved. The nursing knowledge base automatically fuses newest medical research results and clinical nursing practice data. The problems that in the prior art, nursing scheme recommendation accuracy is low, updating is not timely, and the individuation degree is insufficient are effectively solved.

The invention discloses a scene perception personalized recommendation system and method based on time sequence interest evolution. The system comprises a multi-dimensional behavior data acquisition module, a behavior sequence analysis module, a feature engineering module and a recommendation model module. The system collects dominant and recessive behavior data, scene data and content feature data of a user, performs pattern mining on a behavior sequence by using a time weighting mechanism, constructs an interest state transition diagram, and generates a behavior-scene incidence matrix based on the interest state transition diagram. And generating a personalized recommendation result by adopting a DeepFM model fusing a factorization machine and a deep neural network through a multi-level feature fusion strategy. The technical problem that a traditional recommendation system neglects user interest time sequence evolution characteristics and lacks scene perception ability is solved. Time weighted sequence pattern mining and multi-dimensional feature fusion strategies are innovatively introduced, the dynamic change rule of user interests is effectively captured, and the accuracy and timeliness of recommendation results are remarkably improved.

The invention belongs to the field of artificial intelligence and finance, and relates to a personalized copywriting generation method, which comprises the following steps: classifying all customers according to historical customer feature data to obtain different customer groups, obtaining copywriting keyword combinations of the customer groups according to historical copywriting of each customer group, and generating personalized copywriting keywords of the customer groups according to the copywriting keyword combinations; and constructing a customer group portrait according to the customer group characteristics and the customer group document keyword combination, matching a target customer group portrait according to the customer characteristics of the target customer, and generating a product recommendation scheme according to the customer characteristics and the target customer group portrait through a product recommendation model. And generating a personalized copywriting according to the customer features, the customer group portrait features and the product recommendation scheme through the copywriting generation large model. The invention further provides a personalized copywriting generation device, computer equipment and a storage medium. In addition, the invention also relates to a block chain technology, and historical customer feature data can be stored in a block chain. According to the invention, the client can be accurately positioned, and the generated copywriting can be accurately put.

Artificial intelligence-based systems and methods are described for providing personalized skin product recommendations. Natural language data of a user is received using a conversation engine, such as a generative pretrained transformer AI model. The natural language data is input into a synthetic image generation model for generating a digital twin image depicting skin condition(s). A product recommendation model inputs the digital twin image and / or phenotype and / or demographic classifications of the user, and outputs a skin care product recommendation for the user. An image simulation model inputs the product recommendation, the digital twin image and phenotype and / or demographic classifications of the user to output a simulated image of the user having one or more graphical enhancements. The simulated image may be displayed to the user on display screen along with natural language data describing the simulated image as depicted skin as predicted to appear following treatment with the skin care product.

Techniques for selecting medical items for presentation using an artificial intelligence architecture are provided. In one technique, summary note data that is composed by a physician for a patient is received. A machine-learned (ML) language model generates, based on the summary note data, a set of feature values. A profile of the patient and a profile of the physician are identified. An ML recommendation model determines, based on the profile of the patient, the profile of the physician, and the set of feature values, a plurality of candidate medical items. An ML reinforcement learning model generates a ranking of the plurality of candidate medical items. A subset of the plurality of candidate medical items is caused to be presented on a screen of a computing device based on the ranking.

The invention discloses a knowledge enhancement-based neurogenic bladder personalized health management method and system, and relates to the technical field of chronic disease management, and the method comprises the steps: obtaining the illness state information of a neurogenic bladder patient, and obtaining text representation and knowledge graph representation according to the preprocessed illness state information; generating disease condition comprehensive information representation of the patient through splicing and fusion; constructing a chronic disease management scheme library of the neurogenic bladder, performing graph processing on the chronic disease management scheme library, constructing a health management scheme recommendation model based on a graph neural network, taking disease condition comprehensive information representation of the patient as model input, and generating a personalized health management scheme of the neurogenic bladder patient; and according to the personalized health management scheme, regularly performing scheme evaluation, and based on an evaluation score, judging the applicability of the personalized health management scheme. According to the method, the condition characterization of the patient is enhanced by means of massive information knowledge of the knowledge graph, and intelligent recommendation and construction of personalized schemes of chronic diseases of neurogenic bladder patients are enriched.

The invention relates to a personalized learning resource recommendation method based on a subject knowledge graph and a graph neural network, and belongs to the field of education content recommendation. The method comprises the following steps: extracting key entities in a textbook and a relationship between the key entities, and constructing a subject knowledge graph; establishing a preliminary learning record table according to the behavior data of the student in the learning process; through knowledge tracking model integration, predicting mastery degree distribution of students for each knowledge point in the learning record sheet, and constructing student portraits; embedding the knowledge points in the subject knowledge graph and the association relationship thereof into the student portrait, and updating the knowledge embedding representation in the student portrait through a graph neural network; and constructing a personalized recommendation model, and pushing learning resources conforming to the current learning state of the student by adopting a mixed recommendation method of collaborative filtering and content recommendation. According to the method, personalized demands of students can be accurately captured in multiple dimensions, and the accuracy, generalization and interpretability of recommended contents are effectively improved.

The invention relates to an article sequence recommendation method and system based on collaborative filtering and an LLM perspective, and the method comprises the steps: obtaining a user historical data set and an article data set, and carrying out the preprocessing, and obtaining an article title similarity matrix, a user behavior sequence, and historical collaborative filtering interaction information; historical collaborative filtering interaction information is enhanced through a large language model; constructing a sequence recommendation model, inputting the user behavior sequence into the sequence recommendation model for training, and correcting the deviation of the user behavior sequence through comparative learning according to the item title similarity matrix and the enhanced historical collaborative filtering interaction information to obtain a trained sequence recommendation model; and inputting the user behavior sequence of the user into the trained sequence recommendation model for prediction, calculating prediction scores, and generating a recommendation list according to score sorting, thereby completing article sequence recommendation. According to the method, the performance of the sequence recommendation system is greatly improved by solving the cold start problem.

The invention relates to a place recommendation method based on a hypergraph neural network and a diffusion model, and the method comprises the steps: constructing an interest point recommendation model; the interest point recommendation model sequentially comprises a local trajectory flow hypergraph module, spatial-temporal feature coding, a multi-dimensional feature fusion network, a global hypergraph representation learning module, a feature optimization module, an aggregation layer, a frequency domain learning layer and a linear prediction layer. A long track of a user is analyzed and divided into space-time regions, three global hypergraphs are constructed, and the purpose is to comprehensively capture an overall behavior pattern of the user. In order to better optimize trajectory intention characterization, a feature optimization module based on an improved diffusion model is provided. A more stable and controllable reverse process is ensured by introducing multi-dimensional global representation, and an enhanced diffusion model is more suitable for a recommendation system by using a feature normalization and improved Transform network. And designing a loss function to train an interest point recommendation model, wherein the trained interest point recommendation model is used for recommending a next interest point to a new user.

A charging station information providing server receives a charging station recommendation request from an external device, generates, in response to the received charging station recommendation request, a charging station recommendation query based on user information and vehicle information corresponding to a user of the external device, obtains customized recommended charging station information based on charging station list information obtained by using a charging station evaluation model, and road condition information, by using a charging station recommendation model that takes the generated charging station recommendation query as input, and transmits the obtained customized recommended charging station information to the external device.

The invention discloses a credit marketing intelligent recommendation system. According to the system, for the existing credit marketing problem, a customer portrait is constructed through multi-channel data acquisition, cleaning and preprocessing, and personalized credit product recommendation is carried out based on content and collaborative filtering. And training a recommendation model by using a neural network model and a reinforcement learning algorithm, updating recommendation in real time or at regular intervals according to real-time conditions and feedback of customers, establishing a system evaluation effect including recommendation accuracy, customer satisfaction and business indexes, and optimizing model parameters, improving the algorithm and updating data according to evaluation. The method can improve the customer satisfaction and the business conversion rate, reduces the risk, adapts to the market change, and effectively solves the defects of the existing credit marketing channel and strategy.

An inclusive prompt recommendation system for generative AI utilizes an inclusive prompt recommendation model to provide recommendations of inclusive language to include in a prompt in order to promote inclusivity and diversity of generated content. The inclusive prompt recommendation model is trained to analyze input text to identify situations, such as gaming, storytelling, social media, projects or presentations for work / school, and like, where the user's intent is to generate an image or description of a person. The model is trained to identify patterns associated with ways users have historically incorporated inclusive terminology intext. The system can include an ethical filtering mechanism for ensuring that prompt recommendations do not have language that directly or indirectly promotes bias and / or stereotypes.

The invention relates to the field of computer accessories, and discloses a computer accessory intelligent recommendation system and method based on user behavior analysis, and the method comprises the steps: collecting interaction data of a user in an e-commerce platform, and constructing a behavior feature data set of user preference and a current equipment state through a behavior semantic deconstruction algorithm and an equipment configuration recognition mechanism; performing multi-dimensional feature extraction on the behavior feature data set, constructing a user equipment ecological model based on an accessory dependence modeling method, and introducing a behavior context sensing mechanism to perform dynamic learning training on the model; judging whether the recommendation model reaches a stable state or not according to the change trend of the fitting dependence path in the model training process; based on the optimized equipment ecological model, adopting a heterogeneous relation fusion recommendation strategy; and generating a personalized accessory recommendation scheme according to a sorting result, and performing feedback verification on the scheme in combination with a preset recommendation rationality evaluation model. The method has the advantage of improving the precision and experience of accessory recommendation.

The invention relates to the field of big data analysis, in particular to an intelligent recommendation system based on user behaviors. The method comprises the following steps: acquiring user explicit behavior data and user implicit behavior data in a system, acquiring interaction depth index data of a user in the system, adding noise to sensitive behaviors in the data by using a differential privacy technology, establishing a variable time attenuation function, segmenting a behavior sequence according to sessions by using a hierarchical Transform encoder, and obtaining a variable time attenuation function; establishing a GNN user intelligent recommendation model based on a heterogeneous graph neural network, connecting the multi-task learning framework with the heterogeneous graph neural network to obtain a target GNN user intelligent recommendation model, optimizing a recommendation result of the model by using a multi-target optimization algorithm, inputting feature multi-granularity behavior data into the user intelligent recommendation model for recognition, and performing recommendation on the target GNN user intelligent recommendation model. And obtaining a user recommendation result. The problem of recommendation homogenization caused by a single target can be avoided, and intelligent recommendation efficiency and recommendation accuracy are improved.

The invention discloses an article recommendation method and device, a storage medium and electronic equipment. Relates to the field of artificial intelligence, in particular to the field of recommendation systems. Based on the initial implicit collaborative knowledge graph, constructing a plurality of embedded representations, the plurality of embedded representations including an account embedded representation, an article embedded representation and an entity embedded representation; based on the multiple embedded representations, an adaptive implicit collaborative knowledge graph is constructed, and the adaptive implicit collaborative knowledge graph is used for representing potential interests of the account on the articles and implicit preferences and associations between the articles and the entities; based on the self-adaptive implicit collaborative knowledge graph, an article recommendation model is adopted to obtain an article recommendation result corresponding to a target account, and the target account is an account in the self-adaptive implicit collaborative knowledge graph. The object recommendation method and device solve the technical problem that the object recommendation effect is poor due to large sparsity of interaction data and a knowledge graph.

A disclosed method may include initializing a deep learning recommendation model (DLRM) comprising a plurality of embedding tables, each embedding table comprising a plurality of embeddings. The method may also include receiving input data associated with accessing embeddings from the plurality of embedding tables and applying a parallelization strategy to process the plurality of embedding tables, the parallelization strategy configured to improve performance by distributing computational workloads and optimizing memory access. The method may also include processing the embeddings based on the input data in accordance with the parallelization strategy, the processing comprising aggregating embeddings accessed from the plurality of embedding tables. The method may also include generating, for further processing, output data based on the processed embeddings. Various other methods, systems, and computer-readable media are also disclosed.

A data processing method, which can be applied to the field of artificial intelligence. The method comprises: acquiring a prompt, which comprises attribute information of a user, and indicates that an intention of the user is predicted according to the attribute information, and according to a relationship between the intention and a recommendation result obtained by means of a recommendation model, determining a recommendation result to be recommended to the user; by means of the prompt, guiding a large language model (LLM) to determine a target intention of the user according to the attribute information; according to the attribute information and the target intention of the user, obtaining a first recommendation result by means of the recommendation model; and by means of the prompt, guiding the LLM to determine a target recommendation result according to a relationship between the target intention and the first recommendation result. In the present application, an LLM is guided by means of the construction of a prompt to predict an intention of a user, recommendation information is determined according to the intention, attribute information of the user and attribute information of an article, and the LLM is guided by means of the prompt to adjust the recommendation information, such that the recommendation paradigm process can fully utilize the capability of the LLM to assist in recommended information, thereby improving the recommendation precision and effect.