10results about How to "Alleviate data sparsity" patented technology

The invention discloses a traditional Chinese medicine recommendation method based on knowledge driving and a residual attention network, and the method comprises the following steps: S1, a data collection and cleaning module: obtaining prescription text data, executing a standardized process for symptoms and traditional Chinese medicine terms in the prescription text data, and dividing a data set after the standardized process is executed into a training set, a verification set and a test set; s2, an entity pre-training module: extracting symptom semantic features by using a Word2vec training model to extract the features of symptom entities in the prescription and obtain overall context semantic representation, constructing a traditional Chinese medicine attribute knowledge graph, and fusing the natural attribute features of the traditional Chinese medicine as external knowledge into a recommendation model; s3, a multi-graph construction and entity feature learning module: according to a co-occurrence relationship between the symptoms and the traditional Chinese medicines, constructing a symptom-traditional Chinese medicine isomerism graph (SHHG), a symptom-symptom isomerism graph (SSIG) and a traditional Chinese medicine-traditional Chinese medicine isomerism graph (HHIG); respectively extracting symptom features, traditional Chinese medicine features and interaction features from a symptom-traditional Chinese medicine isomeric graph (SHHG), a symptom-symptom isocomposition (SSIG) and a traditional Chinese medicine-traditional Chinese medicine isocomposition (HHIG) by using a graph attention neural network, and enhancing entity features by using a residual structure; the feature fusion and traditional Chinese medicine recommendation module is used for fusing symptom features and traditional Chinese medicine features extracted from a symptom-traditional Chinese medicine isomerism graph (SHHG), a symptom-symptom isomerism graph (SSIG) and a traditional Chinese medicine-traditional Chinese medicine isomerism graph (HHIG) to obtain final symptom features and traditional Chinese medicine features, and expressing the symptom features by using MLP to obtain a traditional Chinese medicine recommendation result. And finally recommending a proper traditional Chinese medicine set according to the given symptom set.

The invention discloses a target abnormal behavior detection and recognition method based on enhanced graph learning. The method comprises the steps of 1, representing target data in a space-time diagram form; 2, designing a parameterized similarity function by using a neural network; step 3, neighbor filtering and global neighbor enhancement are realized based on a top-p sampling method; step 4, aggregating information from all neighbors and relationships based on a message passing graph neural network; 5, based on the idea of mutual information learning, maximizing mutual information between the local representation and the global representation; step 6, constructing a category perception classifier, enhancing the contribution of a small number of samples to the loss function, and weakening the contribution of a large number of samples to the loss function; and 7, performing sequential anomaly detection on the target data. The method is based on an abnormal behavior detection framework of enhanced graph learning, and the target abnormal behavior detection and recognition effect under the interference deception condition is improved.

The invention belongs to the technical field of deep learning and recommendation, and particularly relates to a recommendation method and system based on variational graph auto-encoder denoising, which are used for improving the performance of a recommendation system. The method comprises the following steps: constructing a user-project interaction graph and a knowledge graph; graph embedding initialization is carried out on project nodes in the knowledge graph, and embedding representation of the project nodes is generated; the method comprises the following steps of: enhancing a project of a knowledge graph by using a variational graph auto-encoder to obtain a mean vector and a variance vector of the project, then generating two potential representations of the project through two times of sampling, and reconstructing through a decoder to obtain two knowledge graphs; performing graph contrast learning in the interactive graph, optimizing representations of the user and the item; updating all to-be-learned parameters in the model according to the optimization target; and according to the final embedded representation of the user and the project obtained through optimization, calculating a predicted interaction probability of the user and the project and generating a recommended project.

The invention provides a cancer aided decision-making method based on knowledge-enhanced hypergraph comparative learning, and relates to the technical field of intelligent medical treatment, and the method comprises the steps: carrying out the feature extraction of original drug and cell line data, and constructing a training hypergraph with a drug and a cell line as vertexes and a cooperative relation as a hyperedge; a hypergraph neural network is used for learning, and embedded representation of the combined medicine and cell line is obtained; enhancing the hypergraph so as to carry out comparative learning on the model; performing feature extraction on a to-be-detected data set based on the learned model and updating the hypergraph to obtain an embedded representation of a to-be-detected vertex; and based on the embedding representation of the to-be-detected vertex, obtaining the cooperative relationship of the drugs in the to-be-detected data set. According to the method, the high-order dependency relationship between the medicine and the cell line is considered, good characterization is obtained and is better used for predicting the cooperative relationship, and a good basis is provided for clinical auxiliary decision making.

The present invention discloses a knowledge point automatic annotation modeling method and system, wherein the annotation method includes a unified processing step, an ontology test question acquisition step, an input sample acquisition step, a model training step and a model acquisition step. The present invention trains a unified pre-trained language model UniLM to obtain a knowledge point automatic annotation model, which can take into account the association between labels under the premise of extracting rich semantic information of mathematics test question text, and efficiently completes the prediction of knowledge points of elementary school geometry test questions, thereby improving the credibility of automatic annotation of knowledge points; the present invention proposes an entity recognition and ontology replacement method based on matching templates to alleviate the data sparsity problem of elementary school geometry test question text in model training; the present invention performs full permutation processing on all knowledge point labels contained in the test questions to avoid erroneous feedback caused by inconsistent knowledge point label generation order during training.

The invention discloses a personalized scenic spot recommendation method based on large model picture content feature enhancement, which comprises the following steps: 1, data set acquisition and processing: acquiring a tourism data set with a geographic mark picture, and inversely coding geographic information of the picture to obtain a user-scenic spot interaction matrix; 2, picture content feature extraction: performing data enhancement by using a large model, and converting a picture into interpretable text semantic information; 3, establishing a prediction preference model: adopting a model based on Wideamp; a recommendation model of Deep variants captures a direct relationship between features; and 4, obtaining preference values of the user for all scenic spots from the prediction preference model, sorting the preference values in a descending order, and selecting and recommending the scenic spots corresponding to the top preference values to the user. According to the method, geographic information, time information and picture content attribute information extracted by a large model are integrated, and the design is based on Widamp; according to the recommendation model of the Deep variant, the accuracy and individuation level of scenic spot recommendation are effectively improved.

The present invention provides a method, system, storage medium and electronic device for recommending time-aware hybrid cloud services based on entropy, and relates to the technical field of cloud service recommendation. The present invention includes: time-aware co-evaluation service weight calculation based on entropy; user similarity and trust calculation; QoS indicator score prediction and service recommendation. The cloud service recommendation method can effectively solve the problems of low service QoS prediction accuracy, poor recommendation effect, and data sparsity. Experiments have proved that introducing entropy-based service weights and user trust into traditional recommendation algorithms is feasible and effective, and has certain practical significance.

The application provides a recommendation method, comprising: in response to a recommendation request, obtaining historical interaction data of a request user corresponding to the recommendation request, and determining a to-be-recommended object according to the historical interaction data; for each to-be-recommended object, constructing an interaction pair; for each interaction pair, taking a feature corresponding to the interaction pair as a node, and constructing a default hypergraph corresponding to the interaction pair; inputting the default hypergraph corresponding to the interaction pair into a hypergraph learning model, to predict a hyperedge set of the default hypergraph by a hyperedge generation module, and to model the default hypergraph based on the hyperedge set by a hypergraph learning module, to obtain a complete hypergraph corresponding to the interaction pair, and to predict a probability that a recommended object corresponding to the interaction pair is liked by the request user based on the complete hypergraph corresponding to the interaction pair by a prediction module; and selecting candidate recommended objects from the recommended objects corresponding to each interaction pair according to the probability that the recommended objects corresponding to each interaction pair are liked by the request user, to perform recommendation.

The present invention discloses a project information service recommendation method based on a graph neural network, belongs to the technical field of project information service and recommendation, and comprises the following steps: step 1, obtaining a data set; step 2, constructing a bipartite graph; step 3, constructing an M‑LGACL model, comprising a feature vector initialization module, a Mixup data enhancement module, a lightweight graph convolution layer, a cross attention mechanism, and a contrast loss learning module; step 4, generating a trained M‑LGACL model by continuously iterating the M‑LGACL model constructed in step 3; step 5, evaluating the model performance by calculating the recall rate and the normalized discounted cumulative gain; the project information service recommendation method based on a graph neural network provided by the present invention not only improves the recommendation accuracy of the model, but also effectively solves the problems of data sparsity and insufficient model generalization ability.

The present invention relates to the technical field of reinforcement learning and sequence recommendation, and in particular to a reinforcement learning sequence recommendation method and system based on dual state enhancement. The method comprises obtaining the knowledge graph data of the project and sorting the knowledge graph data according to the timestamp to obtain the interaction sequence; constructing a sequence enhancement model according to the interaction sequence and the project distribution, including constructing a sequence enhancement layer, constructing a knowledge perception enhancement layer and constructing a reward layer; using the constructed sequence enhancement model in combination with the dual state enhancement mechanism, inputting the output of the sequence enhancement model into the Markov decision framework, and updating the model state through the state transition probability; training the sequence enhancement model, and making real-time recommendations to users based on the trained sequence enhancement model. The present invention can better capture the global and local context information in the sequence, enrich the state representation, and alleviate the sparsity problem of the state space.