1857 results about "Recommendation model" patented technology

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 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.

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.

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 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.

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.

The invention discloses a recommendation data enhancement method based on a fine-tuning large language model, and the method comprises the following steps: S1, determining an optimization target of a data enhancement task and designing a corresponding instruction template for a core task in combination with user-article interaction characteristics and auxiliary information in a recommendation system scene; s2, adjusting parameters of the large language model according to related contents of the data enhancement task by adopting a lightweight fine tuning technology to generate data highly aligned with the core task so as to finely tune the large language model; s3, generating new user-article interaction data by fine tuning the large language model, and supplementing article feature information and enhanced data of a project summary; and S4, integrating the enhanced data with the original data of the recommendation model to optimize the training of the recommendation model. According to the method, user-project interaction is enhanced, project attributes are enriched, a high-quality project summary is generated, the problem of data sparsity is effectively relieved, and the generalization ability and recommendation effect of a recommendation system are improved.

The invention discloses a cross-domain privacy protection method and system for advertisement recommendation, equipment and a medium, and the method specifically comprises the steps: carrying out the encryption matching of user behavior data and anonymized equipment data, and generating an initial cross-domain joint feature vector; fusing the initial cross-domain joint feature vector and the disturbance feature vector to form a target cross-domain joint feature vector; splitting a pre-trained advertisement recommendation model into a feature coding sub-module and a reasoning sub-module, deploying the feature coding sub-module to a user adjacent edge node, and retaining the reasoning sub-module in a user local device; and based on the target cross-domain joint feature vector, performing calculation of the feature coding sub-module and calculation of the reasoning sub-module, and uploading the encrypted hidden layer feature vector to a federated learning aggregation server for global model updating. According to the method, cross-domain data utilization and user privacy protection in an advertisement recommendation process are realized, and effective feature vectors are generated for personalized advertisement recommendation while data are guaranteed not to be out of a domain.

A system for personalized product recommendations in an e-commerce platform, comprising: a user data collection module configured to collect browsing history, purchase records, search queries, and demographic information; a data preprocessing module configured to apply cleaning, feature extraction, and natural language processing (NLP) techniques to structure raw data; a product information management module configured to extract, classify, and update product attributes using NLP and image recognition; a recommendation engine that uses machine learning models, including collaborative filtering, content-based filtering, and deep learning techniques, configured to generate personalized product recommendations; a contextual filtering module configured to dynamically refine recommendations based on real-time factors such as user location, device type, and market trends; a feedback learning module configured to continuously update recommendation models using user ratings, reviews, and engagement metrics; and an API integration module configured to enable seamless connectivity with third-party e-commerce applications, inventory management systems, and analytics tools.

The invention discloses an intelligent rehabilitation training system based on multi-parameter detection and a control method, and relates to the technical field of intelligent rehabilitation training, the system comprises a user information acquisition unit, a multi-source parameter acquisition unit, a rehabilitation training plan customization unit, a feedback and control unit and a quantitative evaluation unit; multi-source detection parameters corresponding to movement, physiology and environment of a target user are collected in real time through a preset sensor, the health-related data and the multi-source detection parameters are fused, a rehabilitation training domain knowledge graph is accessed to construct a training scheme recommendation model, and a personalized rehabilitation training plan of the target user is output; according to the achievable degree of the target user for each rehabilitation training action, real-time feedback is provided through a visual interface and voice prompt, and the target user is guided to adjust the training action; a brand new solution is provided for rehabilitation training based on multi-parameter detection, the training state of the user can be monitored in real time, a personalized training scheme is generated, and the rehabilitation effect and efficiency are remarkably improved.

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 discloses a sequence recommendation model based on a bidirectional simplified gated loop network and linear attention. The sequence recommendation model comprises an embedded layer, a bidirectional simplified gated loop network module, an addition attention module, a hybrid expert module and a prediction layer, dynamic preference characteristics of a user are captured by using a bidirectional simplified gated loop network, long-term preference of the user is captured by using an addition attention mechanism, and flexibility and expression ability of the model are enhanced by using a hybrid expert model.

The invention provides an anesthesia virtual simulation training system fusing knowledge, skills and a thinking closed loop. The anesthesia virtual simulation training system comprises a medical knowledge base module, a clinical thinking module, a skill training module, an examination question brushing module, a knowledge graph module and an intelligent platform bottom layer framework. The intelligent platform underlying architecture comprises a data middle platform, an AI engine and a 3D engine, collects student behavior data of each module, constructs a dynamic student ability portrait through a gradient boosting tree algorithm and a collaborative filtering recommendation model, analyzes knowledge blind areas and skill shortages, plans a personalized learning path and pushes targeted training content, and provides a personalized learning result. A closed-loop process of evaluation, learning, practice and re-evaluation is formed; and deep fusion of theoretical knowledge, clinical thinking and skill operation is realized through a cross-module collaboration mechanism. The problems that traditional anesthesia teaching is high in practical operation risk, scattered in resource and insufficient in individuation are solved, and the clinical comprehensive ability and teaching quality of anesthetists are effectively improved.

The invention discloses a registration recommendation method and device, a model training method and device, electronic equipment and a medium, and relates to the technical field of medical service equipment. According to the method, in the processing process of the registration recommendation model, the target first-level department can serve as priori knowledge, so that the accuracy of the registration recommendation model is improved. And the attention mechanism layer can help the registration recommendation model to understand the context information of the second feature and help the registration recommendation model to select the feature which is most contributive to registration recommendation, so that the accuracy of the registration recommendation model is improved. And then a target second-level department is obtained through the effect of the full connection layer and the classifier, the target first-level department and the target second-level department are displayed to the user, and the target second-level department is the department recommended to the user for registration. Thus, according to the voice disease condition information of the user, the second-level department recommended for registration can be automatically generated for the user, registration of the user is facilitated, and the accuracy of the department recommended for registration is high.

A training program recommendation system may monitor a user while the user is performing an assessment exercise activity. An exercise chatbot may present the user with an assessment query. The assessment query may request exercise information for the user. A training program recommendation system may apply a health assessment model to the exercise information to determine a health parameter and a user scaling factor. A training program recommendation system may apply a recommendation model to the health parameter and a user response to the assessment query. The recommendation model generates a training program recommendation for the user based at least in part on at least one of the health parameter or the user scaling factor. The training program recommendation includes a training program. A training program recommendation system may provide the training program recommendation to the user.

Dynamic provisioning of resources in a datacenter improves the utilization efficiency of compute, memory, storage, and network resources, while maintaining flexibility to meet changing demands. Embodiments herein disclose protocol-aware provisioning of resources over Compute Express Link (CXL) fabrics, enabling multi-protocol pooling and disaggregation of resources, including memory and workload-specific accelerators such as GPUs and DSAs. In some embodiments, a Resource Provisioning Unit (RPU) facilitates intent-based protocol translations and mappings between address spaces, potentially enabling the creation of large-scale compute-memory fabrics that may utilize both coherent and non-coherent Non-Transparent Bridging (NTB) between multiple protocols in a single system, serving as the underlying infrastructure for executing workloads such as Large-Language Models (LLMs) and Deep Learning Recommendation Models (DLRMs). Some embodiments also optimize low-latency communication between processes running on different nodes by enabling host-to-host memory provisioning for libraries such as OpenMP, Pthreads, or CUDA, which can utilize shared memory.

Embodiments of the present disclosure provide a model training method and apparatus, a video recommendation method and apparatus, a device, a medium, and a product. The model training method comprises: acquiring a plurality of training samples; using an initial video recommendation model to perform extraction processing on each training sample, so as to obtain a first video interest vector of a user for a target short video and a first item interest vector of the user for a target item; using the initial video recommendation model to perform decoupling processing on the first video interest vector, so as to obtain a first video content interest vector and a first video item interest vector; determining a total loss function on the basis of the first video interest vector, the first item interest vector, the first video content interest vector and the first video item interest vector; and on the basis of the total loss function, training the initial video recommendation model to obtain a video recommendation model.

The invention relates to the technical field of intelligent agricultural information processing, and provides a crop variety suitable planting area recommendation method and device based on deep reinforcement learning, and the method comprises the steps: obtaining crop variety features, environment data of candidate planting areas, and a pre-trained crop variety suitable planting area recommendation model; the recommendation model is trained through the following mode: training a pre-constructed intelligent agent based on variety planting data in different environments and environment data of planting sites and non-planting sites, and obtaining a trained deep reinforcement learning model. And adjusting the trained deep reinforcement learning model based on the variety promotion condition data to obtain a crop variety suitable planting area recommendation model. The recommendation model can accurately match the crop variety and the planting environment according to the input crop variety characteristics and the environment data of the candidate planting area, and accurate recommendation of the crop variety suitable planting area is realized.

The invention belongs to the technical field of article recommendation, and provides an article recommendation method and system based on big language model enhanced graph representation learning, and the method comprises the steps: obtaining a user-article graph recommendation data set; extracting semantic knowledge features of the obtained graph recommendation data set; according to the extracted semantic knowledge features and an article recommendation model, completing article recommendation learning; wherein the article recommendation model adopts a big language model to enhance a graph representation learning model, embeds semantic similarity through the big language model, adopts an adaptive graph structure learning mechanism to identify a semantic boundary so as to determine structure information, and performs bidirectional knowledge interaction transmission on semantic and structure information; interactive noise is filtered and relieved in combination with information bottleneck regularization, optimization of an article recommendation model is carried out with the purpose of minimizing a comprehensive multi-objective loss function, and article recommendation is completed by calculating preference scores of articles.

The embodiment of the invention relates to the technical field of artificial intelligence, and provides an intelligent recommendation method and system based on the plasticizing industry, and the method comprises the steps: obtaining multi-source heterogeneous data from a plurality of third-party data sources of the plasticizing industry; constructing the multi-source heterogeneous data into a dynamic knowledge graph in combination with a plasticizing industry knowledge base; performing demand prediction on the dynamic knowledge graph based on a knowledge-guided hybrid particle swarm algorithm to obtain a first plasticizing recommendation strategy; enhancing a recommendation model through causal reasoning, and performing anti-fact prediction on the dynamic knowledge graph in combination with industry real-time change data to obtain a second plasticizing recommendation strategy; the first plasticizing recommendation strategy and the second plasticizing recommendation strategy are dynamically fused based on Bayesian optimization, a plasticizing industry recommendation report is generated, the plasticizing industry recommendation report comprises the industry flow relation change trend and the corresponding industry hotspot recommendation, and a user is assisted in marketing decision making. The method can shorten the time consumption of the whole supply-demand docking process, effectively alleviates the information asymmetry problem, and remarkably improves the industry operation efficiency.

The invention relates to the field of traditional Chinese medicine clinical auxiliary diagnosis, and discloses an acupuncture point recommendation method, an acupuncture point recommendation model obtaining method, an acupuncture point recommendation model obtaining device, acupuncture point recommendation equipment and a medium, and the acupuncture point recommendation model obtaining method comprises the following steps: collecting patient medical record data to form a medical record data sample set; preprocessing the medical record data sample set to obtain a standard medical record data sample set; inputting the standard medical record data sample set into a preset language model, performing preliminary training on the language model based on the standard medical record data sample set, so that the language model establishes a semantic mapping relationship between the patient symptom information and the acupuncture points, and performing parameter fine tuning and strategy optimization on the preliminarily trained language model in sequence, obtaining an acupuncture point recommendation model; according to the acupuncture point recommendation model obtaining method, the accuracy and individual adaptability of acupuncture clinical treatment are improved, the diagnosis and treatment efficiency is optimized, and a scientific and systematic treatment scheme is provided for a patient.

The invention belongs to the field of artificial intelligence, can be applied to the field of finance, and relates to a product recommendation method, which comprises the steps of classifying historical insurance policies according to a preset dimension, and generating a corresponding classification label for each classification category; matching the customer tags of the potential customers with the classification tags to obtain target classification tags; screening out a target customer group according to the target classification label; inputting the obtained target customer information of the target customer group and the target claim settlement case into a verbal skill recommendation model, outputting a target recommended verbal skill, pushing the target recommended verbal skill to potential customers, and receiving voice data fed back by the potential customers; recognizing the voice data to obtain recognized text data; and matching a target product based on customer demand information extracted from the identified text data, and recommending the target product to potential customers. The invention further provides a product recommendation device, computer equipment and a storage medium. In addition, the invention also relates to a block chain technology, and the client tag can be stored in a block chain. According to the method, clients can be accurately positioned, and the product recommendation effect is improved.

The invention discloses an intelligent recommendation method and system for a corrugated carton packaging production process. The method comprises the steps that a user inputs the type, corrugation type parameters, the number of layers, the size, the weight, production process requirements and quality control standards of corrugated boards through a terminal; the system is based on a cloud computing platform, combines a mechanical property model and a manufacturing process database of a corrugated board, calculates and analyzes input data, and recommends a production process parameter combination; the system collects data in the production process in real time through a sensor, compares the data with recommended parameters, and automatically adjusts parameters of production equipment or gives an alarm. And the system continuously collects production data and user feedback, and updates the recommendation model by using a machine learning algorithm. According to the method and the system, the defective rate is effectively reduced, the production efficiency is improved, the recommendation result is accurate, the management is efficient, and the requirements of corrugated paper field production can be met.

The invention discloses a knowledge graph diffusion recommendation method based on information gain guidance, and the method comprises the steps: carrying out the preprocessing operation of an original knowledge graph and user-article interaction data, calculating information gain, and obtaining a user feature weight matrix; constructing a knowledge graph denoising process based on a diffusion model based on the user feature weight matrix, and training the diffusion model by using an original knowledge graph and user-article interaction data; training a recommendation model by using the de-noised knowledge spectrogram, heterogeneous knowledge aggregation and contrast reinforcement learning to obtain a trained recommendation model; and inputting a to-be-predicted user ID and user-article interaction data into the trained recommendation model to obtain a related recommendation list of the user. By combining the knowledge graph diffusion model, the heterogeneous knowledge aggregation mechanism and the comparative learning strategy, the performance of the recommendation system is remarkably improved.