Kibeom Lee (presenting), Woon Seung Yeo and Kyogu Lee
- focusing on popularity bias - referencing oscar's thesis work (Help! I'm stuck in the head)
- Goal: keep the awesome of collaborative filtering but sort out popularity bias
- the mystery of unpopular but 'loved' songs on last.fm -- shouldn't loved songs be played frequently... perhaps an area of music the user likes but doesn't venture very far into
- 'My tail is your head' - find the users who have a 'head' that overlaps with your 'tail' to draw recs from
- personal story about how this idea came about -- one person's popularity bias is another person's novel rec.
- refs oscar and paul's ISMIR 07 rec tutorial - this system is geared toward the top half of the user type pyramid
- scraped last.fm to get more tracks per user (API gives 50/user scrape gives 500)
- lots of tracks (about 9million)
- eval by asking users how things worked out comparing recs from proposed algor v. trad model rate; used a 1-5 rating scale
- promo'd the website in various ways, but not too much response
- but, the limited response did show some improvement over traditional approach
- overall - some improvement, much potential
Q how many users?: see above
Q so were your recs in the global head?:
sorta, mostly in the midsection
Mark Levy (presenting) and Klaas Bosteels
- an overview of lit showing various rec bias especially the idea of positive feedback reinforcing the head (not this kind of bias though)
- this work looks at 7 billion scrobbles all scrobbles from Jan - Mar this year (holy crap, that's some scale)
- recs just from the last.fm radio
- how do you define the long tail? use a fixed ref of overall artist ranks (number of listeners from last) + a fit model ~50-60k artists in the 'head'
- looked at rec radio, non-rec radio, all music
- the last.fm radio has less head bias then general listening, but only just
- used an experimental cohort of listeners: new, active, but not insane spamming amounts of scrobbling. two subsets : radio users and not so much
- this shows very little difference in the non-radio long tail listening among those who use last.fm radio v. those who don't
- but: perhaps there's some demographic trouble
- so split radio users into high users and low users
- still no tail bias to speak of
- perhaps from the fact that real systems only rec new tracks, mitigating reinforcement
- so: built a simple item-based rec which limited candidates to the 'play direct-from-artist' scheme, not allowed to give artists with more than 10000 fans
- deployed on playground.last.fm
- eval based on a sample of the last.fm user traffic
- effectively pushes curve out another order of magnitude
- try online
- [me: this is great!]
Q Do you see a problem, in terms of scholarship, with the fact that in practice you have access to all this data and the public does not?
well, hrm. how about being an intern
Q Does this make better recs?
Better, eh, interesting sure.
And WOMRAD done. feedback is elicited
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