Recommender systems still mainly base their reasoning on pairwise interactions or information on individual entities, like item attributes or ratings, without properly evaluating the multiple dimensions of the recommendation problem. However, in many cases, like in music, items are rarely consumed in isolation, thus users rather need a set of items, selected to work well together, serving a specific purpose, while having some cognitive properties as a whole, related to their perception of quality and satisfaction, under given circumstances. In this paper, we introduce the term of playlist concept in order to capture the implicit characteristics of joint music item selections, related to their context, scope and general perception by the users. Although playlist consumptions may be associated with contextual attributes, these may be of various types, differently influencing users' preferences, based on their character and emotional state, therefore differently reflected on their final selections. We highlight on the use of this term in HybA, our hybrid recommender system, to identify clusters of similar playlists able to capture inherit characteristics and semantic properties, not explicitly described in them. The experimental results presented, show that this conceptual clustering results in playlist continuations of improved quality, compared to using explicit contextual parameters, or the commonly used collaborative filtering technique.

Peer Reviewed