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Collaborative Filtering Algorithm is one of the most successful recommender technologies, and has been widely used in E-commerce. However, traditional Collaborative Filtering often focus on user-item ratings, but ignore the information implicated in user activity which means how and how often a user makes operations in a system, so it misses some important information to improve the prediction quality...
Collaborative tagging system has become more and more popular and recently achieved widespread success due to flexibility and conceptual comprehensibility of tagging systems. Recommender system has the access to adopt tagging systems to achieve better performance. In this paper we consider that the items can be categorized into different classifications in which users show different interests. Here...
Recommender system emerges as a technology addressing "information overload" problem. Collaborative Filtering (CF) is successful 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...
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, 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 approaches rarely consider the importance of context...
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...
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 filtering problems on two crucial steps: (1)...
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