Online recommender system for radio station hosting based on information fusion and adaptive tag-aware profiling

• A new implicit feedback recommender system for the interactive radio network FMhost.
• A collaborative approach paired with dynamic tag-aware profiles or users and radios.
• An adaptive online learning strategy based on user history and information fusion.
• We compare it with an SVD-based technique in terms of precision, recall, and NDCG.
• Our experiments show that the fusion-based approach demonstrates the best results.

We present a new recommender system developed for the Russian interactive radio network FMhost. To the best of our knowledge, it is the first model and associated case study for recommending radio stations hosted by real DJs rather than automatically built streamed playlists. To address such problems as cold start, gray sheep, boosting of rankings, preference and repertoire dynamics, and absence of explicit feedback, the underlying model combines a collaborative user-based approach with personalized information from tags of listened tracks in order to match user and radio station profiles. This is made possible with adaptive tag-aware profiling that follows an online learning strategy based on user history. We compare the proposed algorithms with singular value decomposition (SVD) in terms of precision, recall, and normalized discounted cumulative gain (NDCG) measures; experiments show that in our case the fusion-based approach demonstrates the best results. In addition, we give a theoretical analysis of some useful properties of fusion-based linear combination methods in terms of graded ordered sets.