Recommender Systems in 2012

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