Faqing Wu

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Collaborative Filtering, one of the most widely used algorithm in recommender system, predicts a user's preference towards an item by aggregating ratings given by users having similar taste with that user. State-of-the-art approaches introduce many other secondary methods to combine to cope with sparsity and precision problem. However, these hybrid(More)
Recommender is a personalized service in the adaptive information system, and it can provide personalized information according to individual information needs. As one of the known technology in the field of the recommender systems, collaborative filtering has been widely used in E-Commerce for its advantages. But the rating prediction mechanism of pure(More)
Collaborative filtering is one of the most successful technologies for building recommender systems, and is extensively used in many personalized systems. However, existing collaborative filtering algorithms have been suffering from data sparsity and scalability problems which lead to inaccuracy of recommendation. In this paper, we focus the collaborative(More)
Collaborative filtering is one of the most successful and widely used methods for automated item recommendation. The most critical component of recommender algorithm is the mechanism of finding similarities among users using item ratings data and so that items can be recommended based on the similarities. The calculation of similarities has relied on(More)
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