Recommender Systems in 2012
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acm recsys 2012:recommender systems, today@neal_lathia
warning:daunting task
lookout for twitter handles
why #recsys?information overload
mailing lists; usenet news (1992)
see: @jkonstan, @presnick
why #recsys?information overload
filter failure
movies; books; music (~1995)
why #recsys?information overload
filter failurecreating value
advertising; engagement; connection (today)
@dtunkelang
(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)
(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”
(3) discovery engines“we are leaving the age of information and
entering the age of recommendation”(anderson)
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
traditional problems
accuracy, scalability, distributed computation, similarity, cold-start, …
(don't reinvent the wheel)
acm recsys 2012:5 open problems
problem 1: predictions
temporality, multiple co-occurring objectives: diversity, novelty, freshness, serendipity,
explainability
problem 2: algorithms
more algorithms vs. more datavs. more rating effort
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
problem 3: users + ratings
signals, context, groups, intents, interfaces
@xamat
problem 4: items
lifestyle, behaviours, decisions, processes, software development
@presnick
problem 5: measurement
ranking metrics vs. usability testingvs. A/B testing
Online Controlled Experiments: Introduction, Learnings, and Humbling Statisticshttp://www.exp-platform.com/Pages/2012RecSys.aspx
3 key lessons
lesson 1: #recsys is an ensemble...of disciplines
statistics, machine learning,human-computer interaction,
social network analysis,psychology
lesson 2: return to the domain
user effort, generative models,cost of a freakommendation,
value you seek to create
@plamere
lesson 3: join the #recsys community
learn, build, research, deploy:@MyMediaLite, @LensKitRS
@zenogantner, @elehack
contribute, read:#recsyswiki, @alansaid
recommender systems, today@neal_lathia