Recommender Systems and Active Learning (for Startups)
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This presentation presents a high level overview of recommender systems and active learning, including from the viewpoint of startups vs. established companies, the cold-start problem, etc.
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Recommender Systems and Active Learning (for Startups)
- 1. Recommender Systems (RS) and Active Learning (AL) Neil Rubens & Dain Kaplan June 2016
- 2. • Value of RS • RS Methods • RS Objectives for Startups • AL for RS 2 Outline
- 3. Value of Recommender Systems
- 5. http://previews.123rf.com/images/lisafx/lisafx1004/lisafx100400069/6903279-Customer-getting-advice-about-tape-from-a-hardware-store-clerk-Isolated-on-white--Stock-Photo.jpg • User Objectives • finding needed item • value • utility • enjoyment • novelty • serendipity • etc. • System Objectives • revenue • profit • promote partners • # of users • # of visits • time spent • etc. Objectives may overlap! 5 RS Objectives
- 6. Value of RS • Amazon: 35% of sales from recommendations • Netflix: 2/3 of the movies watched are recommended • Choicestream: 28% of the people would buy more music if they found what they liked • Google News: recommendations generate 38% more click-throughs www.slideshare.net/kerveros99/machine-learning-for-recommender-systems-mlss-2015-sydney 6
- 7. 7 RS Methods
- 8. User Items Predictions 8 What’s an RS? Sarah Like Like Like LikeHate HateRatings ? ? ? ?
- 9. •Assumption: preferences of “similar” items/users stay similar •Similarity: variety of ways to define 9 Common Approach
- 10. Use ratings to estimate “similarity” 10 Collaborative Filtering (CF) Users Items Ratings Love Like Okay Dislike Hate https://buildingrecommenders.wordpress.com/2015/11/23/overview-of-recommender-algorithms-part-5/
- 11. Users with similar dis/likes are similar, e.g. if Sarah and you have similar tastes, then anything that Sarah likes you will too (and vice versa) Similar items will have similar ratings, e.g., if you liked a book A, you will also like a book B with a similar rating https://buildingrecommenders.wordpress.com/2015/11/23/overview-of-recommender-algorithms-part-5/ 11 Item-based CF User-based CF
- 12. 5" Model& Model"(user)"Based"Es0ma0on" Model"(item)"Based"Es0ma0on" Ac0ve"Learning" 12 MODEL MODEL (USER) BASED ESTIMATION MODEL (ITEM) BASED ESTIMATION ACTIVE LEARNING
- 13. GediminasAdomavicius,AlexanderTuzhilin,"TowardtheNextGenerationofRecommenderSystems:ASurveyoftheState-of-the-ArtandPossible Extensions",IEEETransactionsonKnowledge&DataEngineering,vol.17,no.6,pp.734-749,June2005,doi:10.1109/TKDE.2005.99 Tailored to: • domains • item types • data types • objectives • etc. 13 VARIETY OF RS APPROACHES
- 15. Established Companies Startups “cruise mode” • Many existing loyal users • RS used to increase per- user metrics, e.g. revenue, profit, etc. “launch mode” • Still building user-base • RS used to attract/ retain new users 15
- 16. Startups = Growth “The only essential thing is growth. Everything else we associate with startups follows from growth.” (Paul Graham, Y Combinator) 16
- 17. https://caferacerlaurbanabike.files.wordpress.com/2015/02/new-store-coming-soon.jpg Expecting many: • new users • new items 17
- 18. 18 “Cold Start” Problem ? ? ? ? ? ? ? ? ? ? ? • RS Needs user/item data to make recommendations with CF • For new users/new items, no data is available yet: • New item problem • New user problem New User New Item
- 19. • Problem: don’t have any reviews yet (to base recommendations on) • Solution: can use content-based item similarity (to bootstrap recommendations) 19 New Item Problem Jordan Jumpman Team II Air Jordan 1 Retro High Nouveau Hurley One And Only Printed Air Jordan 1 Retro High OG
- 20. http://cache2.asset-cache.net/gc/558944927-side-view-of-man-opening-cafe-door-gettyimages.jpg?v=1&c=IWSAsset&k=2&d=BrH9aKEkYRiNc1pWhEX0etmgH38bczDi5XkuRcvp%2Bb9LQTmCIaIUqwLdVhpVf%2B9B 20 New User Problem • Very important to make a good first impression: bad first impression may lose potential user • Problem: can’t make personalised recommendations (no data on the user yet)
- 22. http://www.smh.com.au/content/dam/images/2/5/u/n/g/image.related.articleLeadwide.620x349.25ume.png/1347596915177.jpg 22 Learning New User Preferences • Talking: learn about the user implicitly/explicitly • Stalking: obtain data indirectly
- 23. • Contacts: friends may already be users of app (likely to have similar interests) • Location • Device type • Social profile NOTE: should not be intrusive 23 Indirect Data
- 24. http://orangewebsitedesign.com/wp-content/uploads/2015/01/Jim-working-on-website-design-for-client-on-glass-wall.jpg 24 System Interaction Data • How: learn about user through implicit/explicit interaction • clicks (or their absence) • duration • navigation paths • etc. • What: make interaction more informative: item selection • position • attributes • grouping
- 25. Active Learning (AL) for Recommender Systems
- 26. • Recommend an item that a user will like: Popular items, i.e., everyone likes (but provides little info about user’s preferences) • Present an item to learn about user’s preferences (Active Learning, AL): Contentious Items, i.e., many people like / dislike (informative about user’s preferences) 26 Item Selection •RS Presents items for two primary purposes: •In practice multiple items are shown for different objectives
- 27. 27 AL Categories • Item-based AL: analyse items and select items that seem most informative • Model-based AL: analyse model and select items that seem most informative
- 28. • Popular: rated by many users [Rashid 2002] • High Variance in Ratings: item that people either like or hate [Rashid 2002] • Best/Worst: ask user which items s/he likes most/least [Leino & Raiha 2007] • Influential: items on which ratings of many other items depend (representative + not represented) [Rubens & Sugiyama 2007] 28 Item Categories
- 29. c a b input1 input2 d • 3R Properties: • Represented by the existing training set? E.g., (b) is already represented • Representative of others? E.g., (a) is not this way • Results in achieving objective? E.g., (d) → max coverage [Rubens & Kaplan, 2010] 29 Item-based AL
- 30. ! " # !"#$%' !"#$%' ) Illustrative Example: movies are clustered by genre 30 Item Selection: Learning User Preferences
- 31. X1 X2 Limited information due to few items 31 Simply Not Useful
- 32. X1 X1 X2 X2X2 Ratings positive negative System: limited knowledge User: not much variety, may get bored 32 User Satisfaction Drawback
- 33. X1 X1 X2 User exposed to disliked items 33 Coverage Drawback
- 34. 34 decision boundary decision boundary Actual Model Random Sampling Active Learning decision boundary Prediction Accuracy
- 36. g: optimal function (in the sollution space) bf : learned function bfi ’s: learned functions from a slightly di⇣erent training set. EG = B +V +C B = ⇣ Ebf (x) g(x) ⌘2 V = ⇣ bf Ebf (x) ⌘2 C = (g(x) f (x))2 Model Error – C constant and is ignored Bias – B Hard to estimate, but is assumed to vanish (assymptotically). Variance – V Estimate and minize. 10 / 20 36 AL Model Error
- 37. Table 1: Performance comparison of active learning strategies (“XX” Very Good, “X” Good, “ ” Poor, “-” Not Available) ML: Movielens, NF: Netflix, EM: EachMovie, AWM: Active Web Museum, MP: MyPersonality, STS: South Tyrol Suggests, LF: Last.fm Type Strategy Metric Eval. Compar. Strategies Datasets MAE/RMSE NDCG/MAP Precision #Rating Online Offline Non-Personalized Single uncertainty based 1. variance [59, 61] X - - - - y 2, 4, 6, 9, 24 AWM, EM 2. entropy [20, 67] - - - - y 3, 6, 8, 9, 11, 13, 22 EM 3. entropy0 [67] XX - - XX y y 2, 6, 8, 11, 13, 22 ML error reduction 4. greedy extend [68] X - - - - y 2, 3, 6, 7, 10, 11 NF 5. representative [69] - XX XX - - y 6 NF, ML, LF attention based 6. popularity [20, 67] X - - XX y y 2, 8, 9, 11, 13, 22 ML 7. co-coverage [68] - - - - y 2, 3, 4, 6, 10, 11 NF Combined static combin. 8. rand-pop [20, 67] - - y y 2, 3, 6, 11, 13, 22 ML 9. log(pop)*entropy [20] XX - - X y y 3, 6, 8, 13 ML 10. sqrt(pop)*var [68] X - - - - y 2, 3, 4, 6, 7, 11 NF 11. HELF [67] XX - - y y 2, 3, 6, 8, 13, 22 ML 12. non-pers-part rand. [11] X XX X - y 1, 6, 9, 12, 14, 20, 21, 28, 29 ML, NF Personalized Single acquisition prob. 13. item-item [20, 67] - - XX y y 2, 3, 6, 8, 9, 11, 22 ML 14. binary-pred [11, 12] X XX X - y 1, 6, 9, 12, 20, 21, 28, 29 ML, NF 15. personality-based [70, 97] XX XX - XX y y 3, 9, 14 STS, MP 16. impact analysis [71] XX - - - - y 9 ML prediction based 17. aspect model [72, 73] X - - - - y 2 EM, ML 18. min rating [74] X - - - - y 19,25 ML 19. min norm [74] - - - - y 18,25 ML 20. highest-pred [11, 12] X XX X - y 1, 6, 9, 12, 14, 21, 28, 29 ML, NF 21. lowest-pred [11, 12] X X - y 1, 6, 9, 12, 14, 20, 28, 29 ML, NF user partitioning 22. IGCN [67] XX - - X y y 2, 3, 6, 8, 11, 13 ML 23. decision tree [64] XX - - - - y 3, 4, 10, 11 NF Combined static combin. 24. influence based [61] XX - - - - y 1, 4, 6, 9 ML 25. non-myopic [74] X - - - - y 18, 19 ML 26. treeU [75] X - - - - y 23, 27 ML, EM, NF 27. fMF [75] XX - - - - y 23, 26 ML, EM, NF 28. pers-partially rand. [11] X XX X - y 1, 6, 9, 12, 14, 20, 21, 28, 29 ML, NF 29. voting [11, 12] XX XX - y 1, 6, 9, 12, 14, 20, 21, 28 ML, NF adaptive combin. 30. switching [76] XX XX - XX - y 9, 20, 29 ML Mehdi Elahi, Francesco Ricci, Neil Rubens,A survey of active learning in collaborative filtering recommender systems, Computer Science Review, Elsevier, 2016. It is clearly shown in the table that different strategies can improve different aspects of the recom- mendation quality. In terms of rating prediction accuracy (MAE/RMSE), there are various strategies that have shown excellent performance. While, some of these strategies are easy to implement (e.g., Entropy0 and Log(popularity)*Entropy), others are more complex and use more sophisticated Machine Learning algorithms (e.g., Decision Tree, and Personality-based FM). Strategies that have shown excellent per- formance in terms of ranking quality (NDCG/MAP), are Representative-based and Voting strategies. In terms of precision, prediction-based strategies (Highest-predicted, and Binary-predicted) have shown excellent performance. In terms of number of ratings acquired (# Ratings), as expected, strategies that consider the popularity of items (Popularity and Entropy0) can acquire the largest number of ratings. But, other strategies that maximize the chance that the selected items are familiar to the user (Item-item and Personality-based) can also elicit a considerable number of ratings. For these strategies the success ratio (#acquired_ratings/#requested_items) is the largest. This is an important factor, since strategies that only focus on the informativeness of the items may fail to actually acquire ratings, by selecting obscure items that users do not know and cannot rate. Table 1: Performance comparison of active learning strategies (“XX” Very Good, “X” Good, “ ” Poor, “-” Not Available) ML: Movielens, NF: Netflix, EM: EachMovie, AWM: Active Web Museum, MP: MyPersonality, STS: South Tyrol Suggests, LF: Last.fm Metric Eval. Tailored to: •different objectives •different data & settings 37 MANY AL-RS APPROACHES
- 38. http://www.win.tue.nl/~eknutov/gaf.html 38 RS Complexity • RS composed of many modules that need tuning to achieve high performance
- 39. Take-home Messages • RS shows users items they want • RS accounts for a large portion of purchases • RS methods: user/item-based • RS is crucial for user growth, and: • addressing new items/users (“cold start”) with: • indirect data acquisition • content-based item similarity • informative item selection with AL • Many RS components could be tuned to achieve high performance
- 40. EOP