Upcoming talks and travels

I'm going to be up to a number of things that may be of interest to the readers of this (rather sparse) blog.

Quick summary

• Co-chairing The Workshop on Music Recommendation and Discovery (WOMRAD) Oct 23rd, Chicago
• Presenting some new Musicmetric API features during ISMIR, Oct 24-28 (demo on the 28th), Miami
• Giving a talk about beer and critical tasting at Ignite London 5, Nov 8th, London

WOMRAD

Our workshop (other co-chairs are Amélie Anglade, Òscar Celma, Paul Lamere, and Brian McFee) will cover a diverse array of approaches and angles for music recommendation and discovery. The workshop is runs the full and is part of RecSys 2011, though I sadly can't stay for most of the conference aside from our workshop (see below). It should prove to be an interesting day of research. Are you planning on attending? Let us know.

ISMIR

I'll be dashing off from Chicago to Miami to attend ISMIR, and to present some new (not-yet-released) API features from Musicmetric. While things aren't quite live yet, I can say that in addition to our artist based endpoints, we'll be offering track-based endpoints soon as well, and aligning them with an I-bet-you-can-guess-which large public audio feature test set. More detail on this one to come.

Ignite London 5

Ignite is a series of lightning talks that have taken place in cities all over the world, unified not by a common theme, but a common format: all talks last five minutes, contain 20 slides, and the slides are automatically advanced every 15 seconds. The matching ethos of this structure is perhaps best seen in the Ignite slogan, "Enlighten us, but make it quick." I'll be speaking about beer, style and critical tasting in a talk titled "Ale or Lager and Other False Choices." Here's a brief description:

In a word, my talk is about beer. In a few more words, the driving narrative behind the talk is a crash course in beer styles and more generally, critical tasting. After an extraordinarily brief description of beer, broad ideas of style and the critical tasting process, the core of the talk will be made up of live lightning tastes of commercial examples of various styles of beer (one slide per style, 12 styles covered with one commercial example each). For coherence these tasting slides will grouped into broader styles, with aim toward width, rather than depth of coverage. The styles will be approximately based on those from the BJCP and the Brewer's Association.

I still haven't sorted out the exact spread of beers or how they will be grouped, though I'm leaning toward something simple and obviously ingredient tied (something like -- Lagers, ales:yeast driven, ales:malt forward, ales:hop heavy, with 3 beers each from a different recognized style in each). If anyone has any thoughts about style divisions or specific examples do let me know. If you'd like to go to ignite (and you know you would) the tickets will be available over this way later this week.

So, lots of things going on. Plus there's this other thing I've been working on.

Right, back to it.

WOMRAD Afternoon live blog

Afternoon live blog for WOMRAD. Intro in first post.

Afternoon session.

1500: Industrial panel

panelists:

• Òscar Celma (BMAT, Spain), moderator
• Tom Butcher (Microsoft Bing, US)
• Mark Levy (last.fm, UK)
• Michael S. Papish (Rovi Corporation) subing for Henri-Pierre Mousset (Yozik, France)
• Gilad Shlang (Meemix, Israel)

Q (asked by OC): Do we need recommenders anymore? Are they relevant? (SFMusicTech quote about music rec only needed for people w/o friends)

TB: still valid, but personally more interested in discovery now...

ML: don't sell things, but tremendous effort in this direction, important to users, builds trust. Plus we compliment not replace social connections

MP: no need to draw lines, reinforces complimentary service idea. Many users may not need them, but perhaps that's not who these systems are for

GS: What's wrong with not having friends? Also, a tight group of friends may not have discovery, as a group. The removal of place opens more possibility to access long tail or different parts of the head. Perhaps more personalization than rec, but this a fine line

MP: the opposition of the individual v. group. If you listen to music without a community you loss the social experience.

GS: fair enough. some points about individual optimization of education. Group important, but also personal growth.

Q (asked by OC): Netflix prize. What is a good recommendation (in music)? How do we evaluate (in music)?

GS: a good rec will get people interested. wow factor. acknowledge you and surprise you at same time. music is short , which makes it easier to tune a rec profile. sharing implies liking, that's useful. tagging; more tags=more popular

(...small aside...)

ML: we run controlled experiments. quietly divide users to test different methods. Netflix fails in that it evaluates with data that's already been seen not new data.

MP: good rec means different things at different times. gives an example of a good rec that is not interesting: an artist you've previously bought releases a new album. not interesting but good. this would be bad for radio.

TB: in industry there are many ways to test. more purchase is different than more enjoyment.

ML: we at last would love for some theory to be developed for rec based on user logs.

OC: more data please

ML: you can always ask...

(this goes back and forth)

MP: yet there will never be good data. sparse data is hard, but makes your a better human (eat your green)

Q (OC) discuss user interfaces, user exerience etc. :

TB: pandora is a winner, don't ignore the interaction

ML: thesixtyone is great. interface v. playful good long tail, would love for a last.fm interaction, but discussing issues

MP: name checks Paul lamere, who he cites saying thesixtyone is an exotic rec, but MP thinks this is the way people normally use music, we should work to have systems that act like this. Need a toolbelt not one ubiquitous tool

GS:we tackle similar things. in B2B you need systems that complete clients' existing systems. If you over rec, you can scare people off, social dynamics

MP: think about the inverse rec - what should you not rec? Also from a UX stand point, to build trust, change recs over time. General to personal as a user interacts with a system

GS: different recs can be very personal

ML: last.fm takes a sort of opposite

-crosstalk-

MP: this is possible from last.fm's transparency

Q (OC) What do you want solved?

TB: we're hiring. Also, algorithms must scale or they're scalable

ML: how to merged datasources? how to use human-to-human recs

MP: see my keynote, exploit user psych, What are good Qs to ask users to build profiles

GS: more info for recs, params in audiofiles. map user params to extractable params Moving techniques to non-western musics. What about china and india? We should be serve them.

MP: is the sonic data really the key? I don't think so, too much effort in this direction sim is different than rec

GS: but sim is a good start

Q(OC) Do you use content-based features (y/n please)? ISMIR fun, glass ceiling, do you follow this work in academics, do you think it's solved?

GS: Yes. see last discussion core to our business, vital to start a relationship, move to social and such over time.

OC: what sorts of things do you use

GS: of things. 10 (does not list). aggressiveness very important.

ML: I come from the MIR community. we do content-based ID, have tried to intro content-based stuff and it's never been successful. But our hearts are in it. We have enough users that cold start doesn't matter. auto-tagging would be sweet though for the holes in our social data (musicological tags for instance). maybe youtube

TB: yes for the most part to ML's comments. content-based is too costly, tags and metadata are super effective

GS: what about the new company, that doesn't have lots of data. is you're just getting into the game. these people need results. can't tell them go gather data for a year and we'll sort it out

MP: item to item is very different than personalized

ML: check that P2P paper from ismir

Hannah from Franhauffer: we have clients (like film producers) no data,what then?

MP: exactly.

(Eugenio Tacchini): GS is the DNA all of it? really?

GS: no, not really. music DNA for rich space, but still need personalized info

Q (OC) if you were to hire a researcher (aside: research cannot program) what kind skills do you want (not resume skills, fancy skills)?

TB: domain experience, audio music, computer vision, breadth better than depth. production coding skill in some language

ML: we're hiring as well. If you don't want to code, probably won't work. CS skills really important. Big database skills. hadoop win. strong C++ and research also python. data and viz as well.

MP: we are hiring as well. growing r & d group. we have offices all over. we like building things. though we have room for research. we like solving problems. again broad. can you pivot. don't need a PhD to be useful.

GS: we're also hiring (that's 4 for 4) we're a start up. data analysis and mining. core CS + creative skills, willing to sweat.

OC: perhaps also adoptability

GS: yes. you're there to invent. plus we're in Tel Aviv and that's sweet

Q (claudio): What is the relationship your company has with musicians are they just a commodity?

OC: our missing speaker (Henri) does this

GS: I spoke with him, he thinks: for young musicians it's hard to reach your audience.

OC: BMAT does this with jamendo. when they type in 'Michael Jackson' what do you do?

MP: but don't sell recsys as a way to push new artists only. In a certain context, ie. neg search, but careful. Don't exploit users or artists

GS: but the state of the art pushes new bits

(from audience) What about piracy?

GS: it's not good.

TB: there are 2 view points: piracy increases consumption. otherside: do we now that?

OC: now we're over time sorry.

---

in light of the near transcript I just typed, I'm starting a new post for the afternoon talks.

Updated (5:33) : corrected questioner ID

A womrad live blog

I'm in Barcelona today for the Workshop on Music Recommendation and Discovery (WOMRAD). The theme is 'Is Music Recommendation Broken? How Can We Fix It?'

I'm giving a talk at 11am ( in about 2 hours ) and I'll be doing some (mostly) live updates about the program...

Update (10:05am):

Keynote: "The Dark Art: Is Music Recommendation Science a Science?"

UPDATE ( 28 Sept 2010, 11:52am): Michael has posted the slides to his talk.

• The view from outside, as his industrial has used and observed recs
• Been there since the beginning (which appears to be about 2000)
• Recommenders must combine humans and machines
• understand both content and listeners, transparency, embrace emosocio aspects, optimize trust
• What is science? Must be falsifiable (Popper) or Solvable, reproducible puzzles (gah, missed name)
• Puzzle - understand the listeners preferences -- foundations (ISMIR 2001 resolution) - testable reusable
• Lots of metrics though (too many?) (do we need a metric for metrics?)
• MIREX (summary of AMS task) (haha it's automated, tell that to andy and mert) - very acoustically focused, not exactly recommendation similarity != recommendation
• use of statistical measures across datasets e.g. Netflix prize -- but what about discovery? -- Netflix produces better numbers but does it produce better recommendations?
• More holistic measures -- survey users about trust and satisfaction (Swearingen & Sinha) -- may miss UI issues -- practical 'business' metrics -- bottomline measurements -- does this remove the science?
• appreciated history of MIR (from a rec POV) will stick pic here -- currently hitting 'Wall of Good Recs' since recs don't suck it's no harder to test
• easy to test for bad recs -- hard to test for good recs
• What if the emerging problems (like UI and trust) are no longer measurable
• Is user preference too variable and unstable to be useful?
• from science to art?
• 2 options:
• 1: focus on unsolved MIR: better encoding of preference (more socio-cultural research)
• What are the limits of the avg listener (hey it's our playlist survey!)--playlist turing tests, understand artist v. album v. tracks -- can we build tools/games to expand this
• listener profile -- can you quantify the sonic v. social preference -- add relevance layers to search and retrieval
• 2: adjourn to the Beach
• Questions:
• Mark Levy: Do you think you're too embarrassed about good engineering? What about controlled experiments by people like google/last? -- Move from science to engineering (this confuses me slightly ISMIR has alway been Engineering not Pure Science) It is fruitful but is it science.
• Claudio: Can you speak a bit about your experience combining human knowledge vs. algorithms --- yes. what do you do with human knowledge? it's tricky. look for the ideal rec experience - sit around with your friends and play records: how do you scale that in a system? It's not about classification - humans are good at putting things together - train people to be qualitative assessors
• Oscar: Since you used to be in college radio, how do you think this experience could inform playlist? Do you use playlisters? Well only a 1.5yr experience, but made me think about the groups of listeners. Name checks John Peel. What about presentation - In terms of what rovi does: Minimally we can stop making bad playlists: gives example then breaks - v. hard to differentiate btwn good - v. good - excellent
• Me: what about bypassing order by selecting good sets:
• (Eugenio Tacchini): how much is the expert transparency necessary? yes give justification but need to avoid the feeling of stereotyping, weird vague directions, not just look at this user but look at this part of this user.
• tom butcher: Is music rec really a unique snowflake? - Every domain is unique. -- One thing: a bad user rec in music costs 2 minutes, a bad film rec costs you 2hrs music has a lower penalty cost for bad recs. Also diff in features will sonic features get you to pref, prob not in music [I think this is a think which may improve...]

(update 2 10:31am)

session 1:

Time Dependency

Rocking around the clock eight days a week: an exploration of temporal patterns of music listening -- [Perfecto Herrera], Zuriñe Resa and Mohamed Sordo

• personal ex. showing diff between early day v. late night playlist
• trying to link 2 concepts - Day- hour - (weather?) and Music track selection
• few papers on this idea -- take things from Human Dynamics -- trying to enable playing music 'at the right moment' -- explore circular stats
• Circular stats (eqs in paper at link) basically transform raw data by a perodicity (days, weeks)
• Circular stats have analogous tools to trad stats - hyp tests for instance
• Data for eval is full listening history of 992 unique last.fm users with artist/title + time of day (ToD) also got genre via track.getTopTags, keeping genre -- discarded users w/o enough data
• scraped about half the data
• attempt to make predictions - use two years of data to predict the ToD of play in next year
• results: by day about 2.5x better than chance, by hour about 3-5x better than chance (move from half hour to hour tolerance doubles data
• note that the figures are overall, some users are v. predictable in this way, some are not.
• Concl: temporal patterns can be predicted - not just what but when. plugs the last.fm clocks
• Q (dunno who asked): what about user to user offsets (eg. if a user gets up at 6 v 8am 8 am means something different)? Currently can't do this, need sensor data. Would be sweet if we could, though not tha tthe predictions are peruser, so this is to some effect already dealt with
• Q (again, people say who you are): Method issue - when comp day v. hour there's a percentile diff in the err tolerance? Sure this could work look at baseline compare...
• Q (Eugenio Tacchini): I tried this awhile ago, aggregated data, didn't find much spread do you think aggregation is the issue? yeah, must be specific to the user, right time + right user not just right user
• Q (Klaas):do you think it would work with less data (can't wait 2 years)? Probably. This was a very conservative methodology, could probably get by with maybe three months. For this work we wanted lots of data to make things clear
• Q (seriously ID guys): did you use a popularity filter? No. tested if pref for a genre is different than the average for that genre

Break time then my talk. no notes for my talk as I'm talking...

Update (12:16): I was without my machine for the social tag session, not just my talk. I'll get my hand notes in another post but for now here are the papers:

• Using Song Social Tags and Topic Models to Describe and Compare Playlists. Benjamin Fields, Christophe Rhodes and Mark d'Inverno
• Piloted Search and Recommendation with Social Tag Cloud-Based Navigation. Cédric Mesnage and Mark Carman
• A Method for Obtaining Semantic Facets of Music Tags. Mohamed Sordo, Fabien Gouyon and Luís Sarmento

Update ( ):

next paper is being skipped since the author was unable to attend due to illness:

Survey of Music Recommendation Aids. Pirkka Åman and Lassi Liikkanen

Now joining the presentation already in progress by Audrey Laplante:

The Role People Play in Adolescents' Music Information Acquisition.

• qualitative study of adolescents
• 'Did your music taste change significantly in the last three years?' Yes, whys: New boyfriend, New school therefore new friends, important discussion topic
• "who in your 'gang' or group exert the most influence on others in terms of music?" -- 3 self-identified. Characteristics: highly invested in music, good comm, willing to share info. People who are opinion leaders want to stay opinion leaders, will invest heavily in effort
• in other domains work shows that weak ties are more important then strong ties in finding new information works almost all the time -- for 2 participants weak ties important -- for others strong ties with significantly different social network are important -- music as vehicle for social interaction
• strong ties have different roles -- not important for discover, but critical for legitimization of musical taste
• similar and reliable social connections are critical
• social network maps (pic forthcoming...)
• unknown how common these results are (same survey) as yet unknown exact implications for recommenders
• Q (unknown)- Weak ties v. Strong ties -- how do you define the difference?: not really about newness, but it's entirely possible with new detail
• Q (claudio) - What kinds of systems are implied with this work? Not necessarily a different system for adolescents. tight connectivity is critical, perhaps the difference is that strong ties may become more critical
• (claudio) - does the notion that music describes you change as you get older?: not really actually, adolescent are interested in individual uniqueness
• Q (Mark L) are social networks online somewhat different?: yes and no. in facebook you can find relatives, but noise is a big problem. But trust is not known
• I asked about using graph difference. Answer could work, also other automatic methods...

lunch now. I'll make a new post for the afternoon session.

updated again (5:14pm) Eugenio Tacchini is Italian not Finnish (oops)