Recommender systems are an effective solution to the information overload problem, specially in the online world where we are constantly faced with inordinately many choices. These systems try to find the items such as books or movies that match best with users’ preferences. Based on the different approaches to finding the items of interests to users, we can classify the recommender systems int...

A context-aware recommender system incorporates the knowledge of different contextual factors such as time or weather information to improve item suggestions made to a user. This requires the system to have a large knowledge base for inferring contextual information and enabling accurate and timely recommendations. We present a versatile approach for a context-aware recommender system in the to...

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...

Recently, there has been growing interest in recommender systems (RSs) and particularly in context-aware RSs. Methods for generating context-aware recommendations were classified into the pre-filtering, post-filtering and contextual modeling approaches. This paper focuses on comparing the pre-filtering, the post-filtering, the contextual modeling and the un-contextual approaches and on identify...

The goal of the work presented in this paper is to design a context-aware recommender system for mobile devices. The approach is based on decentralized, item-based collaborative filtering on Personal Digital Assistants (PDAs). The already implemented system exchanges rating vectors among PDAs, computes local matrices of item similarity and utilizes them to generate recommendations. We then expl...