HengSong Tan

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Collaborative filtering systems represent services of personalized that aim at predicting a user’s interest on some items available in the application systems. With the development of electronic commerce, the number of users and items grows rapidly, resulted in the sparsity of the user-item rating dataset. Poor quality is one major challenge in(More)
Collaborative filtering (CF) technique has been proved to be one of the most successful techniques in recommender systems. Two types of algorithms for collaborative filtering have been researched: memory-based CF and model-based CF. Memory-based approaches identify the similarity between two users by comparing their ratings on a set of items and have(More)
User modeling is a key technology in implementing personalized services. This paper tried to solve disadvantage of lack of semantic information of keyword, and designed a user profiles modeling method based on the category knowledge base, combining the keywords and the ontology concepts. In the model the user profiles consist of short-term interest and(More)
Mobile users often post nearest neighbor queries based on their current location. Usually, the mobile terminal (user) sends a request to query an untrusted location server, including the position information of the mobile terminal requests, thus leading to the disclosure of one’s location. For mobile users providing location services, the privacy of mobile(More)
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