As the use of recommender systems becomes more consolidated on the Net, an increasing need arises to develop some kind of evaluation framework for collaborative filtering measures and methods which is capable of not only testing the prediction and recommendation results, but also of other purposes which until now were considered secondary, such as novelty in the recommendations and the users' t...

Trust-aware recommender system (TARS) recommends ratings based on user trust. It greatly improves the conventional collaborative filtering by providing reliable recommendations when dealing with the data sparseness problem. One basic research issue of TARS is to improve the recommending efficiency, in which the key point is to find sufficient number of recommenders efficiently for active users....

In view of information overflow on the web, the use of recommender systems seems to be an appropriate means by which to organize information that targets preferences. The purpose of this article is to present a novel model explaining the satisfaction with recommender websites integrating emerging influential factors such as trust, exploratory browsing, and personal factors. Three recommender sy...

Many trust-aware recommender systems have explored the value of explicit trust, which is specified by users with binary values and simply treated as a concept with a single aspect. However, in social science, trust is known as a complex term with multiple facets, which have not been well exploited in prior recommender systems. In this paper, we attempt to address this issue by proposing a (dis)...

recommender systems (rs) provide personalized recommendation according to user need by analyzing behavior of users and gathering their information. one of the algorithms used in recommender systems is user-based collaborative filtering (cf) method. the idea is that if users have similar preferences in the past, they will probably have similar preferences in the future. the important part of col...

Although demonstrated to be efficient and scalable to large-scale data sets, clustering-based recommender systems suffer from relatively low accuracy and coverage. To address these issues, we develop a multiview clustering method through which users are iteratively clustered from the views of both rating patterns and social trust relationships. To accommodate users who appear in two different c...

Initial successes in the area of recommender systems have led to considerable early optimism. However as a research community, we are still in the early days of our understanding of recommender systems. Evaluation metrics continue to be refined but we still need to account for the relative contributions of the various knowledge elements that play a part in the recommendation process. In this pa...