Music Recommendation and playlisting
Session Chair: Douglas Turnbull
CONTINUOUS PLSI AND SMOOTHING TECHNIQUES FOR HYBRID MUSIC RECOMMENDATION
by Kazuyoshi Yoshii and Masataka Goto
- Unexpected encounters with unknown songs is increasingly important.
- Want accurate and diversifed recommendations
- Use a probabilistic approach suitable to deal with uncertainty of rating histories
- Compares CF vs. content-based and his Hybrid filtering system
Approach: Use PLSI to create a 3-way aspect model: user-song-feature – the unobservable category regading genre, tempo, vocal age, popularity etc. – pLSI typical patterns are given by relationships between users, songs and a limited number of topics. Some drawbacks: PLSI needs discrete features, multinomial distributions are assumed. To deal with this formulate continuous pLSI, use gaussian mixture models and can assume continuous distributions. A drawback of continuous pLSI – local minimum problem and the hub problem. Popular songs are recommended often because of the hubs. How to deal with this: Gaussian parameter tying – this reduces the number of free parameters. Only the mixture weights vary. Artist-based song clustering: Train an artist-based model and update it to a song-based model by an incremental training method (from 2007).
Here’s the system model:
Evaluation: They found that using the techniques to adjust model complexity significantly improved the accuracy of recommendations and that the second technique could also reduce hubness.
STEERABLE PLAYLIST GENERATION BY LEARNING SONG SIMILARITY FROM RADIO STATION PLAYLISTS
François Maillet, Douglas Eck, Guillaume Desjardins, Paul Lamere
This paper presents an approach to generating steerable playlists. They first demonstrate a method for learning song transition probabilities from audio features extracted from songs played in professional radio station playlists and then show that by using this learnt similarity function as a prior, they are able to generate steerable playlists by choosing the next song to play not simply based on that prior, but on a tag cloud that the user is able to manipulate to express the high-level characteristics of the music he wishes to listen to.
- Learn a similarity space from commercial radion staion playlists
- generate steerable playlists
Francois defines a playlist. Data sources: Radio Paradise and Yes.com’s API. 7million tracks,
Problem: They had positive examples but didn’t have an explicit set of negative examples. Chose them at random.
Learning the song space: Trained a binary classifier to determine if a song sequence is real.
Features: Timbre, Rhythmic/dancability, loudness
Eval:
EVALUATING AND ANALYSING DYNAMIC PLAYLIST GENERATION HEURISTICS USING RADIO LOGS AND FUZZY SET THEORY
Klaas Bosteels, Elias Pampalk, Etienne Kerr
Abstract: In this paper, we analyse and evaluate several heuristics for adding songs to a dynamically generated playlist. We explain how radio logs can be used for evaluating such heuristics, and show that formalizing the heuristics using fuzzy set theory simplifies the analysis. More concretely, we verify previous results by means of a large scale evaluation based on 1.26 million listening patterns extracted from radio logs, and explain why some heuristics perform better than others by analysing their formal definitions and conducting additional evaluations.
Notes:
- Dynamic playlist generation
- Formalization using fuzzy sets. Sets of accepted songs and sets of rejected songs
- Why last two songs not accepted? To make sure the listener is still paying attention?
- Interesting observation that the thing that matters most is membership in the fuzzy set of rejected songs. Why? Inconsistent skipping behavior.
SMARTER THAN GENIUS? HUMAN EVALUATION OF MUSIC RECOMMENDER SYSTEMS.
Luke Barrington, Reid Oda, Gert Lanckriet
Abstract: Genius is a popular commercial music recommender sys- tem that is based on collaborative filtering of huge amounts of user data. To understand the aspects of music similarity that collaborative filtering can capture, we compare Genius to two canonical music recommender systems: one based purely on artist similarity, the other purely on similarity of acoustic content. We evaluate this comparison with a user study of 185 subjects. Overall, Genius produces the best recommendations. We demonstrate that collaborative filter- ing can actually capture similarities between the acoustic content of songs. However, when evaluators can see the names of the recommended songs and artists, we find that artist similarity can account for the performance of Genius. A system that combines these musical cues could generate music recommendations that are as good as Genius, even when collaborative filtering data is unavailable.
Great talk, lots of things to think about.
Music Machinery 