When users generate music playlists: When words leave off, music begins?

Abstract

Music systems that generate playlists are gaining increasing popularity, yet ways to select songs to be acceptable to users is still elusive. We present the results of an explorative study that focused on the language of musically untrained end users for playlist choices, in a variety of listening contexts. Our results indicate that there are a number of opportunities for playlist recommendation or retrieval systems, particularly by taking context into account.

Listen or interact? A Large-scale survey on music listening and management behaviours

“…an inconsistency with what Stump and Muscroft observed [27] as they found the order of importance to be tempo, mood, genre. The most popular attribute from the "other" field was lyrics/content, which was mentioned 10 times.…”

“…Music consumption research includes studies on information seeking behaviours [1,9,18,15], browsing and accessing music [6,2], playlist generation [27,12], finding new music [4], contexts and purposes of listening [7,17,27,23], social listening [27,7,23], and library management [29,5]. In this chapter, a selection of these studies will be introduced and discussed in conjunction with the topics covered in our survey.…”

“…In 2011, Stumpf and Muscroft [27] studied playlist creation behaviours of 7 participants, for 3 different purposes (large party, small gathering, private travel). They found out that tempo, mood, and genre were the most important factors when creating playlists for private travel, which is the only personal context studied and thus of interest to us.…”

“…Other frequently mentioned activities included shower/bathroom (32), gaming (27), before/after sleep (24), and reading (20).…”

Effects of recommendations on the playlist creation behavior of users

“…Choice of tracks for playlists in general Finally, a number of studies in the literature are concerned with understanding the factors that influence which tracks users choose for inclusion in a playlist in different situations (Pauws 2002;Swearingen and Sinha 2002;Cunningham et al 2006Cunningham et al , 2007Lehtiniemi 2008;Lamont and Webb 2011;Lee et al 2011;Stumpf and Muscroft 2011) and (Kamalzadeh et al 2012). For example, the results of an early user study conducted by Pauws (2002) showed that playlists containing personalized tracks that were selected automatically for particular contexts ("listening to soft music" and "listening to lively music") were preferred by users over randomly assembled playlists.…”