Recommender Systems in 2012
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acm recsys 2012:recommender systems, today@neal_lathia
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warning:daunting task
lookout for twitter handles
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why #recsys?information overload
mailing lists; usenet news (1992)
see: @jkonstan, @presnick
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why #recsys?information overload
filter failure
movies; books; music (~1995)
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why #recsys?information overload
filter failurecreating value
advertising; engagement; connection (today)
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@dtunkelang
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(1) collaborative“based on the premise that people looking for
information should be able to make use of what others have already found and evaluated”
(maltz & ehrlick)
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(2) query-less“in September 2010 Schmidt said that one day the
combination of cloud computing and mobile phones would allow Google to pass on
information to users without them even typing in search queries”
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(3) discovery engines“we are leaving the age of information and
entering the age of recommendation”(anderson)
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input: ratings, clicks, viewsusers → items
process: SVD, kNN, RBM, etc.f(user, item) → prediction ~ rating
output: prediction-ranked recommendations
measure:|prediction – rating|(prediction – rating)2
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traditional problems
accuracy, scalability, distributed computation, similarity, cold-start, …
(don't reinvent the wheel)
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acm recsys 2012:5 open problems
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problem 1: predictions
temporality, multiple co-occurring objectives: diversity, novelty, freshness, serendipity,
explainability
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problem 2: algorithms
more algorithms vs. more datavs. more rating effort
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what is your algorithm doing?f(user, item) → R
f(user, item1, item
2) → R
f(user, [item1...item
n]) → R
e.g., @alexk_z@abellogin
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problem 3: users + ratings
signals, context, groups, intents, interfaces
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@xamat
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problem 4: items
lifestyle, behaviours, decisions, processes, software development
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@presnick
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problem 5: measurement
ranking metrics vs. usability testingvs. A/B testing
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Online Controlled Experiments: Introduction, Learnings, and Humbling Statisticshttp://www.exp-platform.com/Pages/2012RecSys.aspx
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3 key lessons
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lesson 1: #recsys is an ensemble...of disciplines
statistics, machine learning,human-computer interaction,
social network analysis,psychology
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lesson 2: return to the domain
user effort, generative models,cost of a freakommendation,
value you seek to create
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@plamere
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lesson 3: join the #recsys community
learn, build, research, deploy:@MyMediaLite, @LensKitRS
@zenogantner, @elehack
contribute, read:#recsyswiki, @alansaid
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recommender systems, today@neal_lathia