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In this paper, we focus on how to overcome several limitations in the traditional research of collaborative filtering(CF). We present a novel CF recommendation algorithm, named DSNP(Dynamic Similar Neighbor Probability). This algorithm improves the neighbors’ similarities computations of both users and items to choose the neighbors dynamically as the recommendation sets. How to select the confident...
Nowadays collaborative filtering technologies are widely used in many websites, while the majority research literatures focused on improving recommendation accuracy. However, it had been recognized that improving recommendation accuracy was not the only requirement for achieving user satisfaction. One important aspect of recommendation quality, recommendation diversity gained focus recently. It was...
In this paper, we focus on how to overcome cold-start problem in the traditional research of recommendations system(RS). The popular technique of RS is collaborative filtering(CF). While in real online RS, CF can't practically solve cold-start problem for the sparsity ratings dataset. In this paper, we propose a novel efficiently association clusters filtering(ACF) algorithm. Considering hybrid approaches,...
In recent years, extensive researches have been conducted to develop approaches to answer two major challenges for collaborative filtering problems, namely sparsity and scalability. In this paper, we propose a novel collaborative filtering recommendation approach to alleviate these challenges. Our approach firstly converts the user-item ratings matrix to user-class matrix, and hence increases greatly...
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