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In this paper, we propose a novel method combined classical collaborative filtering (CF) and bipartite network structure. Different from the classical CF, user similarity is viewed as personal recommendation power and during the recommendation process, it will be redistributed to different users. Furthermore, a free parameter is introduced to tune the contribution of the user to the user similarity...
Providing accurate predictions efficiently with privacy is imperative for both customers and e-commerce vendors. However, privacy, accuracy, and performance are conflicting goals. Although producing referrals with privacy is possible; however, online performance and accuracy degrade due to underlying privacy-preserving measures. We investigate how to improve both efficiency and accuracy of naive Bayesian...
Expert Collaborative Filtering is an approach to recommender systems in which recommendations for users are derived from ratings coming from domain experts rather than peers. In this paper we present an implementation of this approach in the music domain. We show the applicability of the model in this setting, and show how it addresses many of the shortcomings in traditional Collaborative Filtering...
Social tagging systems pose new challenges to developers of recommender systems. As observed by recent research, traditional implementations of classic recommender approaches, such as collaborative filtering, are not working well in this new context. To address these challenges, a number of research groups worldwide work on adapting these approaches to the specific nature of social tagging systems...
Like search engines, recommender systems have become a tool that cannot be ignored by websites with a large selection of products, music, news or simply webpages links. The performance of this kind of system depends on a large amount of information. At the same time, the amount of information on the Web is continuously growing, especially due to increased User Generated Content since the apparition...
This paper proposes a hybrid approach of personalized Web Information Retrieval that utilizes (1) ontology for retrieval of user's context (2) user profile that is temporarily updated according to users' browsing behavior and (3) collaborative filtering for considering recommendation of similar users. Empirical analysis reveals that Precision, Recall and F-Score of most of the queries for many users...
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