Recommender systems have evolved from addressing information overload in mailing lists and usenet news in the 1990s to now helping with advertising, engagement, and connection. They have moved from collaborative filtering based on user ratings to being query-less and able to anticipate user interests without searches. While traditional problems include accuracy, scalability, and cold starts, five open problems are predictions over time, balancing algorithms and data, understanding users and ratings, modeling items, and measuring recommendations beyond rankings. The key lessons are that recommender systems draw from many disciplines, solutions must consider the domain, and participating in the community is important.

1. acm recsys 2012:
recommender systems, today
@neal_lathia

2. warning:
daunting task
lookout for twitter handles

3. why #recsys?
information overload
mailing lists; usenet news (1992)
see: @jkonstan, @presnick

4. why #recsys?
information overload
filter failure
movies; books; music (~1995)

5. why #recsys?
information overload
filter failure
creating value
advertising; engagement; connection (today)

6. @dtunkelang

7. (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)

8. (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”

9. (3) discovery engines
“we are leaving the age of information and
entering the age of recommendation”
(anderson)

12. input: ratings, clicks, views
users → items
process: SVD, kNN, RBM, etc.
f(user, item) → prediction ~ rating
output: prediction-ranked recommendations
measure:
|prediction – rating|
2
(prediction – rating)

13. traditional problems
accuracy, scalability, distributed computation,
similarity, cold-start, …
(don't reinvent the wheel)

14. acm recsys 2012:
5 open problems

15. problem 1: predictions
temporality, multiple co-occurring objectives:
diversity, novelty, freshness, serendipity,
explainability

17. problem 2: algorithms
more algorithms vs. more data
vs. more rating effort

18. what is your algorithm doing?
f(user, item) → R
f(user, item1, item2) → R
f(user, [item1...itemn]) → R
e.g., @alexk_z
@abellogin

19. problem 3: users + ratings
signals, context, groups, intents, interfaces

20. @xamat

21. problem 4: items
lifestyle, behaviours, decisions, processes,
software development

22. @presnick

23. problem 5: measurement
ranking metrics vs. usability testing
vs. A/B testing

24. Online Controlled Experiments: Introduction, Learnings, and Humbling
Statistics
http://www.exp-platform.com/Pages/2012RecSys.aspx

25. 3 key lessons

26. lesson 1: #recsys is an ensemble
...of disciplines
statistics, machine learning,
human-computer interaction,
social network analysis,
psychology

27. lesson 2: return to the domain
user effort, generative models,
cost of a freakommendation,
value you seek to create

28. @plamere

29. lesson 3: join the #recsys community
learn, build, research, deploy:
@MyMediaLite, @LensKitRS
@zenogantner, @elehack
contribute, read:
#recsyswiki, @alansaid

30. recommender systems, today
@neal_lathia