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
is the cost to add song m to list after song n, 
is some measure of distance from song n to song m and 
is the same measure of distance from song m to song e. Song e is the end song for the whole playlist. So basically the idea is that the cost of moving to a node is a ratio of how far away it is from where you were to how far it is to where you're trying to get. The whole thing is reversed for the other half, so the cost function makes it cheap to move toward the start song. If you simply want to randomly traverse social links the cost can be set to an arbitrary equal value (I used 1) for all links.
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