5results about How to "Alleviate cold start problems" patented technology

This invention relates to the field of exercise prescription generation and recommendation technology, and discloses an LLM-based generative recommendation method for exercise prescriptions. The method involves acquiring and preprocessing data, constructing a knowledge graph based on the preprocessed data (S1), pre-training the LLM model, inputting the constructed knowledge graph into the LLM model, and optimizing it through reinforcement learning. The PPO algorithm is used to optimize the LLM model's output strategy. Based on the optimized LLM model, the exercise prescription is derived and generated in natural language using FITT parameters. This method combines user characteristics and scenario information to generate high-quality, personalized exercise prescriptions. The invention utilizes a multi-dimensional reward function framework to quantify prescription quality and optimize the generation strategy, ensuring that the generated exercise prescriptions are not only scientifically sound but also conform to the user's actual situation and preferences. Synthetic data is generated through a user simulator, achieving an upgrade from static recommendation to dynamic adaptation, improving user experience and the level of intelligence in exercise and health management.

The application discloses a user cold start recommendation system and method based on similarity matching, constructs a knowledge graph database according to users, products and the interaction relationship between the users and the products, and carries out standardization processing. The processed data is extracted to score information and mapped to a user like-dislike type space, so that the like-dislike product type of each user is determined, and a similarity matching algorithm is designed to find the old user with the highest similarity to the new user. The old user product type score vector and the new user product type vector score are processed by an association function and used as the recommendation basis of the system, the reasonable expansion of the new user data is completed, and the cold start problem of the recommendation system is effectively alleviated. The application solves the user cold start problem caused by the introduction of a new user into an industrial recommendation system. The application can be used for the scene of effectively recommending the product types that meet the preferences of new users in the industrial recommendation system.

Embodiments of the present disclosure relate to a media resource recommendation method, medium and product, wherein the method comprises: in response to a resource recommendation request for a target object, first screening media resources of interest of the object based on object information to form an initial recommendation result; then selecting part of the target type media resources according to the urgency of the target type media resources that need to be recommended in an emergency, updating the initial recommendation result, and further recommending the updated recommendation result to the target object; in this way, by synergistically integrating the object interest-oriented media resources and the target type media resources with high urgency, the recommendation experience of the target object can be ensured, the cold start problem of the target type media resources that need to be recommended in an emergency can be alleviated, and the exposure rate of the target type media resources can be improved.

The application discloses a battery state of health estimation method based on symmetric positive definite manifold and logarithmic Euclidean registration and electronic equipment, and relates to the field of battery state of health estimation; covariance matrices of each source domain battery sample are geometrically modeled on a symmetric positive definite manifold by using affine invariant Riemannian metrics, and are unified to a same registration center by adopting a logarithmic Euclidean registration mechanism, so that covariance structure heterogeneity caused by differences in production batches of battery cells, vehicle purposes or regional environment is eliminated, the adaptability of the model to unseen working conditions or new batches of batteries is significantly enhanced, and the SOH estimation generalization capability in cross-working-condition and cross-batch scenes is effectively improved.

The application relates to the technical field of medical dispatching, and provides a personalized doctor-patient matching method and system based on TTA-LDG in a known disease category scene. First, a personalized doctor recommendation model TTA-LDG is constructed, and neighbor aggregation is performed on patient embedding and doctor embedding by a light-weight graph convolutional neural network in the personalized doctor recommendation model TTA-LDG facing a doctor-patient interaction graph, so as to output an interaction side representation of the patient, a doctor interaction side representation, and the like. In addition, personalized attention propagation is performed on patient feature queries by a personalized knowledge graph attention subnetwork in the personalized doctor recommendation model TTA-LDG facing a doctor knowledge graph, so as to output a knowledge side doctor representation. Then, a gating mechanism is used to perform representation alignment and fusion on the patient interaction layer representation, the doctor interaction side representation and the knowledge side doctor representation, so as to obtain a matching probability of the patient and the doctor.