3 results about "Low-rank approximation" patented technology

The invention provides a user portrait recommendation method based on a high-order structure and semantic enhancement, and the method specifically comprises the following steps: S1, introducing a multi-hop adjacent matrix to capture a high-order behavior pattern in an interaction graph for the interaction graph of a user and a project through a high-order structure maintenance module of user grouping, and employing a low-rank approximation and clustering method to obtain a high-order behavior pattern in the interaction graph; grouping the users based on the behavior similarity; s2, extracting a representative keyword set from items interacted by the same group of users, and obtaining group-level keywords of the users; s3, through a portrait perception recommendation module based on cross-view comparative learning, user semantic embedding and project semantic embedding based on keywords are constructed; obtaining user structure embedding and project structure embedding according to a collaborative structure between a user and a project in the interaction graph; then, alignment of project semantic embedding and project structure embedding is achieved through cross-view comparative learning; and S4, calculating a user-project combination score and generating a recommendation result. According to the invention, the recommendation performance is enhanced.

The application discloses a style legal consultation question and answer method and system, a storage medium and equipment, relates to the technical field of natural language processing, and the method comprises the following steps: collecting text data and audio and video data in the legal consultation field and converting the text data and audio and video data into texts; generating structured text data sets from the texts by using a base model; dividing the structured text data sets into a basic legal knowledge base and a stylized knowledge base according to the style categories of seed instructions; adopting different styles of vertical field labeled training sets, and obtaining a pre-training fine-tuning model by fine-tuning the base model based on a low-rank approximation fine-tuning method; and generating a corresponding style answer from the pre-training fine-tuning model according to a received user legal consultation question. Through the technical scheme of the application, the stylized model can be iterated more quickly, the low-delay question and answer demand is realized, the legal consultation reply can be provided according to the style preferred by the user, and the work efficiency of legal consultation is improved.

The application discloses a kind of based on low rank contrast learning and reconstruction graph anomaly detection method, comprising: 1) obtain graph data, and generate low rank graph data by SVD singular value decomposition dimension reduction, using restart random walk algorithm to carry out subgraph sampling, obtain original view and low rank view;2) on original view and low rank view, construct contrast pair and carry out contrast learning, obtain contrast learning loss;3) low rank attribute reconstruction is carried out on original view and low rank view, and the final reconstruction loss of original view and low rank view is obtained;4) the graph neural network model is trained in combination with contrast learning loss and reconstruction loss;5) according to the trained graph neural network model, the abnormality of node in the graph to be measured is judged, and the potential abnormal node is determined.The application generates low rank view by low rank approximation to node attribute and topological structure, effectively filters the interference of abnormal node and noise on the basis of retaining the original structure of graph.