Best Usage Context Prediction for Music Tracks

Recommender Systems (RSs) are software tools and techniques providing suggestions for items to be of use to a user. Often, better recommendations can be generated if the of the recommendation is known, e.g., in a music RS, the user mood or activity. However, to adapt the recommendations to the the dependency of the user preferences from the contextual conditions must be modeled. This requires explicit user evaluations/ratings for items in alternative contexts. In this work we investigate a novel approach for collecting and using contextually dependent ratings in recommender systems. We introduce the concept of \best context, i.e., the contextual conditions most suited for a particular item to be recommended. We designed an interface for collecting such data for music tracks. The collected data was then used to evaluate the quality of several \best context prediction methods based on user-to-user collaborative ltering. The results, in opposition to what we expected, show that the notion of best is user dependent. Moreover, among the approaches we tried, the best performing one uses a k-nearest neighbors classier where the user-to- user similarity measures the agreement of two users in assigning the best to items.