2results about How to "Realize personalized recommendation" patented technology

This application relates to a method and system for recommending urban park locations based on crowd activity preferences. The method includes: responding to a park location recommendation instruction by detecting user-input information; and obtaining a recommendation result based on a knowledge graph and a trained park location recommendation model, whereby the recommendation result includes at least one location. The knowledge graph is constructed based on a park location feature dataset and a crowd activity preference dataset; the park location recommendation model is trained using a deep learning model based on the knowledge graph; and the park location feature dataset includes multi-dimensional sensory environment data and spatial structure data of all locations within the park. This method enables personalized recommendations for park locations.

This invention discloses a method for recommending official document approval opinions based on semantic matching and role awareness, comprising the following steps: S1. Constructing a historical document vector database and a structured metadata database; S2. Obtaining documents to be approved and performing feature extraction and preliminary screening; S3. Performing text block-level similarity retrieval on the documents and aggregating them; S4. Calculating job matching scores based on job information; S5. Calculating the final recommendation score and sorting the documents in descending order based on the final recommendation score, outputting the top 5 with the highest scores as the recommended results. This invention improves recommendation accuracy by using a pre-trained semantic embedding model to understand the deep semantics of document content. Furthermore, through job matching score calculation and approval node position alignment mechanisms, it deeply binds the recommended approval opinions to specific approval positions and approval process positions, achieving personalized recommendations.