Generating personalized recommendations for new users is particularly challenging, because in this case, the recommender system has little or no user record of previously rated items. Connecting the newcomer to an underlying trust network among the users of the recommender system alleviates this socalled cold start problem. In this paper, we study the effect of guiding the new user through the ...

Traditional recommender systems usually ignore the social interactions between users in a social network and assume that users are independent and identically distributed. This assumption hinders the users to have access to personalized recommendations based on their circle of trusted friends. To model the recommender systems more accurately and realistically, we propose a social trust model an...

a r t i c l e i n f o Collaborative Filtering (CF) is the most popular recommendation technique but still suffers from data sparsi-ty, user and item cold-start problems, resulting in poor recommendation accuracy and reduced coverage. This study incorporates additional information from the users' social trust network and the items' semantic domain knowledge to alleviate these problems. It propos...

Recommender systems are essential to overcome the information overload problem in professional learning environments. In this paper, we investigate interest-based recommendation in academic networks using social network analytics (SNA) methods. We implemented 21 different recommendation approaches based on traditional Collaborative Filtering (CF), Singular Value Decomposition (SVD)-based RS, Tr...

Existing online mentorship systems typically match mentors and mentees manually. Recommender systems can be used to match mentors and mentees and trust and reputation mechanisms can be used to improve the decision process. This paper discusses the state-of-the-art in online mentorship systems, recommender systems, and trust and reputation mechanisms. It further proposes a five-stage process for...

the evaluation and selection of recommender systems is a difficult decision making process. this difficulty is partially due to the large diversity of published evaluation criteria in addition to lack of standardized methods of evaluation. as such, a systematic methodology is needed that explicitly considers multiple, possibly conflicting metrics and assists decision makers to evaluate and find...

Today, the trust has turned into one of the most beneficial solutions to improve recommender systems, especially in the collaborative filtering method. However, trust statements suffer from a number of shortcomings, including the trust statements sparsity, users' inability to express explicit trust for other users in most of the existing applications, etc. Thus to overcome these problems, this ...

In this paper, we present FilmTrust, a website that integrates Semantic Web-based social networks, augmented with trust, to create predictive movie recommendations. We show how these recommendations are more accurate than other techniques in certain cases, and discuss this technique as a mechanism of Semantic Web interaction. Trust in social networks on the Semantic Web is a topic that has gain...

ISSN 2250 – 107X | © 2011 Bonfring Abstract--Recommender system helps people to find information or items that they needed. Collaborative Filtering (CF) is an eminent technique in recommender systems. CF uses relationships between users and recommends items to the active user based on the ratings of his/her neighbors. But, there are several drawbacks in CF like data sparsity problem, where user...

Trust has been used to replace or complement ratingbased similarity in recommender systems, to improve the accuracy of rating prediction. However, people trusting each other may not always share similar preferences. In this paper, we try to fill in this gap by decomposing the original single-aspect trust information into four general trust aspects, i.e. benevolence, integrity, competence, and p...