User-based context modeling for music recommender systems

Abstract

One of the main issues that have to be considered before the conception of context-aware recommender systems is the estimation of the relevance of contextual information. Indeed, not all user interests are the same in all contextual situations, especially for the case of a mobile environment. In this paper, we introduces a multi-dimensional context model for music recommender systems that solicits users’ perceptions to define the relationship between their judgment of items relevance and contextual dimensions. We have started by the acquisition of explicit items rating from a population in various possible contextual situations. Next, we have applied the Multi Linear Regression technique on users’ perceived ratings, to define an order of importance between contextual dimensions and generate the multi-dimensional context model. We summarized key results and discussed findings that can be used to build an effective mobile context-aware music recommender system.