Abstract
Music recommender systems have become a key technology to support the interaction of users with the increasingly larger music catalogs of on-line music streaming services, on-line music shops, and personal devices. An important task in music recommender systems is the automated continuation of music playlists, that enables the recommendation of music streams adapting to given (possibly short) listening sessions. Previous works have shown that applying collaborative filtering to collections of curated music playlists reveals underlying playlist-song co-occurrence patterns that are useful to predict playlist continuations. However, most music collections exhibit a pronounced long-tailed distribution. The majority of songs occur only in few playlists and, as a consequence, they are poorly represented by collaborative filtering. We introduce two feature-combination hybrid recommender systems that extend collaborative filtering by integrating the collaborative information encoded in curated music playlists with any type of song feature vector representation. We conduct off-line experiments to assess the performance of the proposed systems to recover withheld playlist continuations, and we compare them to competitive pure and hybrid collaborative filtering baselines. The results of the experiments indicate that the introduced feature-combination hybrid recommender systems can more accurately predict fitting playlist continuations as a result of their improved representation of songs occurring in few playlists.
Highlights
Music recommender systems have become an important component of music platforms to assist users to navigate increasingly larger music collections
– We introduce two feature-combination hybrid recommender systems – readily applicable to automated music playlist continuation, – able to exploit any type of song feature vectors
The song feature vectors are extracted from song audio clips gathered from the content provider 7digital,8 and from social tags and listening logs obtained from the Million Song Dataset (MSD)9 (Bertin-Mahieux et al 2011), a public database providing an heterogeneous collection of data for a million contemporary songs
Summary
Music recommender systems have become an important component of music platforms to assist users to navigate increasingly larger music collections. As in other item domains, music recommender systems often provide personalized lists of suggestions based on the users’ general music preferences This approach may work to recommend music entities such as albums, artists, or readymade listening sessions (like curated playlists or charts) because it can be useful to provide the users with a wide choice range. A common approach to explicitly address the automated continuation of music playlists consists in applying Collaborative Filtering (CF) to curated music playlists, revealing specialized playlist-song co-occurrence patterns (Aizenberg et al 2012; Bonnin and Jannach 2014) While this approach works fairly well, it has an important limitation: the performance of any CF system depends on the availability of sufficiently dense training data (Adomavicius and Tuzhilin 2005). The proposed systems can be used to play and sequentially extend music streams, resulting in a lean-back listening experience similar to traditional radio broadcasting, or to assist users to find fitting songs to extend their own music playlists, stimulating their engagement
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