Incorporating Context into Recommender Systems Using Multidimensional Rating Estimation Methods
نویسندگان
چکیده
Traditionally recommendation technologies have been focusing on recommending items to users (or users to items) and typically do not consider additional contextual information, such as time or location. In this paper we discuss a multidimensional approach to recommender systems that supports additional dimensions capturing the context in which recommendations are made. One of the most important questions in recommender systems research is how to estimate unknown ratings, and in the paper we address this issue for the multidimensional recommendation space. We present the classification of multidimensional rating estimation methods, discuss how to extend traditional twodimensional recommendation approaches to the multidimensional space, and identify research directions for the multidimensional rating estimation problem.
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