4results about How to "Realize personalized recommendation" patented technology

The invention relates to a display device and a personalized recommendation reason generation method. The display apparatus includes: a display configured to display a user interface; an audio output device configured to output sound; the controller is configured to display a search result page in response to a multimedia content search operation triggered by a target user; in the process that the target user carries out multimedia content search for the search result page, target multimedia content is selected; a personalized recommendation reason of the target multimedia content is obtained, and the personalized recommendation reason is generated based on the user portrait data of the target user and the media asset data of the target multimedia content; and controlling at least one of the display and the audio output device, and displaying the personalized recommendation reason. By adopting the display equipment, the conversion rate of a multimedia content search function can be improved.

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.

The embodiment of the invention provides a recommendation method and system and a storage medium. The recommendation system obtains interaction data generated when a target user uses a first group of functions of a service platform, and extracts portrait features of the target user from the interaction data. The recommendation system generates a guide instruction based on the portrait features, and inputs the guide instruction into a first large language model to guide the first large language model to perform reasoning based on the portrait features and determine a target function meeting the demand of the target user, function description information of all functions in a second group of functions of the service platform is injected into the first large language model, and the first large language model is configured to refer to the function description information of the second group of functions in the reasoning process, and the functions meeting the requirements of the target user are determined in the second group of functions. And a recommendation system recommends the target function to the target user through the service platform.

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.