Dynamic Playlist Generation based on Skipping Behavior
Sprache des Vortragstitels:
Englisch
Original Tagungtitel:
6th International Conference on Music Information Retrieval (ISMIR 2005), London, UK
Sprache des Tagungstitel:
Englisch
Original Kurzfassung:
Common approaches to creating playlists are to randomly
shuffle a collection (e.g. iPod shuffle) or manually select
songs. In this paper we present and evaluate heuristics
to adapt playlists automatically given a song to start with
(seed song) and immediate user feedback.
Instead of rich metadata we use audio-based similarity.
The user gives feedback by pressing a skip button
if the user dislikes the current song. Songs similar to
skipped songs are removed, while songs similar to accepted
ones are added to the playlist. We evaluate the
heuristics with hypothetical use cases. For each use case
we assume a specific user behavior (e.g. the user always
skips songs by a particular artist). Our results show that
using audio similarity and simple heuristics it is possible
to drastically reduce the number of necessary skips.
Forschungs-