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Collaborative filtering can predict an active user's interests for unrated items based on his observed ratings, and the issue of concept drift exists in most of recommender systems. Aiming at the issue of concept drift, a time-enhanced collaborative filtering approach is proposed in this work, in which a time weight is introduced into the framework of collaborative filtering. As the experimental results...
Collaborative filtering can estimate users' ratings for unvisited items based on the opinions about items implied in their observed ratings. The issue of sparsity induced by the insufficiency of rating is a key factor impacting the recommendation accuracy. Aiming at the issue of sparsity, a balanced collaborative filtering approach is proposed in this work. According to the conformity of users, the...
As a widespread approach in recommender systems, item-based collaborative filtering can predict an active user's interest for a target item based on his interest and the ratings for those similar items to his visited items. As the effect of human's conformity psychology, an individual user's judgment usually tends to follow the general view. The majority of existing item-based collaborative filtering...
Collaborative filtering (CF) is one of the most successful technologies in recommender systems, and widely used in many personalized recommender applications, such as digital library, e-commerce, news sites, and so on. However, most collaborative filtering algorithms suffer from data sparsity problem which leads to inaccuracy of recommendation. This paper is with an eye to missing data imputation...
Collaborative filtering (CF) is one of the most successful technologies in recommender systems, and widely used in many personalized recommender areas, such as e-commerce, digital library and so on. However, most collaborative filtering algorithms suffer from data sparsity which leads to inaccuracy of recommendation. In this paper, we focus on nearest-neighbor CF algorithms and propose a new collaborative...
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