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Recommender system algorithm and architecture

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Liang Xiang
Liang Xiang
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Recommender System: Algorithms & Architecture xiangliang@hulu.com
Outline Problem Data • Algorithms • Cold start • Architecture • • Recommender System
Problem Recommend items to users to make user, content partner, websites happy!
Data • User behaviors data Page view All user Very Large Behavior User Size Watch video All user Large Favorite Register user Middle Vote Register user Middle Add to playlist Register user Small Facebook like Register user Small Share Register user Small Review Register user Small
Data • Which data is most important Page view All user Very Large Behavior User Size – Main behavior in the Watch video All user Large website Favorite Register user Middle Vote Register user Middle – All user can have such Add to playlist Register user Small behavior Facebook like Register user Small – Cost Share Register user Small Review Register user Small – Reflect user interests on items
Data • Data Structure – User ID – Item ID – Behavior Type – Behavior Content – Context • Timestamp • Location • Mood Sheldon watch Star Trek with his friends at home
Algorithms Recommender System Method Collaborative Content Social …… Filtering Filtering Filtering Latent Factor Graph-based …… Model Neighborhood …… -based User-based Item-based ……
Neighborhood-based • User-based – Digg • Item-based – Amazon, Netflix, YouTube, Hulu, …
User-based • Algorithm – For user u, find a set of users S(u) have similar preference as u. – Recommend popular items among users in S(u) to user u.
User-based CF pui = ∑ v∈S ( u , K ) ∩ N ( i ) wuv rvi N (u ) ∩ N (v) w uv = N (u ) ∪ N (v)
Item-based • Algorithm – For user u, get items set N(u) this user like before. – Recommend items which are similar to many items in N(u) to user u.
Item-based CF pui = ∑ j∈S ( i , K ) ∩ N ( u ) w ji ruj N (i ) ∩ N ( j ) w ij = N (i ) ∪ N ( j )
Item-based CF Why not use w ij = ? N (i ) ∩ N ( j ) N (i )
Neighborhood-based • User-based vs. Item-based User-based Item-based Scalability Bad when user size is Bad when item size is large large Explanation Bad Good Novelty Bad Good Coverage Bad Good Cold start Bad for new users Bad for new items Performance Need to get many Only need to get users history current user’s history
References • Amazon.com Recommendations item-to- item Collaborative Filtering. • Empirical Analysis of Predictive Algorithms for Collaborative Filtering.
Graph-based • Users’ behaviors on items can be represented by bi-part graph. A 1 A 1 A 1 A 1 B 2 B 2 B 2 B 2 C 3 C 3 C 3 C 3 D 4 D 4 D 4 D 4
Graph-based • Two nodes will have high relevance if – There are many paths in graph between two nodes. – Most of paths between two nodes is short. – Most paths do not go through nodes with high out-degree.
Graph-based • Advantage – Heterogeneous data A 1 • Multiple user behaviors • Social Network B 2 • Context (Time, Location) C 3 • Disadvantage D 4 – Statistical-based – High cost for long path
References • A Graph-based Recommender System for Digital Library. • Random-walk computation of similarities between nodes of a graph with application to collaborative recommendation.
Latent Factor Model • Users and items are connect by latent features. A 1 a B 2 b C 3 c D 4
Latent Factor Model rui = ∑ puk qik ˆ k Science Fiction 0.5 Science Fiction 0.9 Universe 0.9 Universe 0.9 Physical 0.8 Physical 0.5 Space Travel 0.8 Space Travel 0.7 Animation 0.3 Animation 0.1 Romance 0.0 Romance 0.0
Latent Factor Model • How to get p, q? min ∑ (rui − ∑ puk qik ) + λ ( pu + qi ) 2 2 2 ( u ,i ) k = α (eui qik − λ puk ) puk + = α (eui puk − λ qik ) qik +
Latent Factor Model • How to define rui – Rating prediction – Top-N recommendation • Implicit feedback data: only have positive samples and missing values, how to select negative samples?
Latent Factor Model 1 (Sci-fi) 2 (Crime) 3 (Family) 4 (Horror) The Blair Witch The invisible Man Jaws 101 Dalmatians Project Frankenstein Back to the Meets the Wolf Lethal Weapon Pacific Heights Future Man Godzilla Total Recall Groundhog Day Stir of Echoes Star Wars VI Reservoir Dogs Tarzan Dead Calm The Terminator Donnie Brasco The Aristocats Phantasm The Jungle Book Alien The Fugitive Sleepy Hollow 2 Alien 2 La shou Shen tan Antz The Faculty
Latent Factor Model • Advantage – High accuracy in rating prediction – Auto group items – Scalability is good – Learning-based • Disadvantage – Incremental updating – Real-time – Explanation
References • http://www.informatik.uni- trier.de/~ley/db/indices/a- tree/k/Koren:Yehuda.html
Cold Start • Problems – User cold start : new users – Item cold start : new items – System cold start : new systems
User Cold Start • How to recommend items to new users? – Non-personalization recommendation • Most popular items • Highly Rated items – Using user register profile (Age, Gender, …)
User Cold Start • Example: Gender and TV shows Data comes from IMDB : http://www.imdb.com/title/tt0412142/ratings
User Cold Start Male Age : 20-30 Theoretical physicist Doctor American Irreligious
How to get user interest quickly • When new user comes, his feedback on what items can help us better understand his interest? – Not very popular – Can represent a group of items – Users who like this item have different preference with users who dislike this item
Item Cold Start • How to recommend new items to user? – Do not recommend How to recommend news??
Item Cold Start • How to recommend new items to user? – Using content information Machine Data Mining Recommendation Learning
System Cold Start • How to design recommender system when there is no user? – Pandora : Music Genome Project – Jinni : Movie Genome Project
Architecture • Feature-based recommendation framework: A 1 a B 2 b C 3 c D 4 User Feature Item
Architecture Male Scientist Physics
Architecture • Advantage: – Heterogeneous data – Reasonable Explanation • Disadvantage: – Do not support user-based methods
Open Questions • How to weight multiple behaviors? • How to improve diversity, novelty? • How to build feedback loop?
Thanks!

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Recommender system algorithm and architecture

  • 1. Recommender System: Algorithms & Architecture xiangliang@hulu.com
  • 2. Outline Problem Data • Algorithms • Cold start • Architecture • • Recommender System
  • 3. Problem Recommend items to users to make user, content partner, websites happy!
  • 4. Data • User behaviors data Page view All user Very Large Behavior User Size Watch video All user Large Favorite Register user Middle Vote Register user Middle Add to playlist Register user Small Facebook like Register user Small Share Register user Small Review Register user Small
  • 5. Data • Which data is most important Page view All user Very Large Behavior User Size – Main behavior in the Watch video All user Large website Favorite Register user Middle Vote Register user Middle – All user can have such Add to playlist Register user Small behavior Facebook like Register user Small – Cost Share Register user Small Review Register user Small – Reflect user interests on items
  • 6. Data • Data Structure – User ID – Item ID – Behavior Type – Behavior Content – Context • Timestamp • Location • Mood Sheldon watch Star Trek with his friends at home
  • 7. Algorithms Recommender System Method Collaborative Content Social …… Filtering Filtering Filtering Latent Factor Graph-based …… Model Neighborhood …… -based User-based Item-based ……
  • 8. Neighborhood-based • User-based – Digg • Item-based – Amazon, Netflix, YouTube, Hulu, …
  • 9. User-based • Algorithm – For user u, find a set of users S(u) have similar preference as u. – Recommend popular items among users in S(u) to user u.
  • 10. User-based CF pui = ∑ v∈S ( u , K ) ∩ N ( i ) wuv rvi N (u ) ∩ N (v) w uv = N (u ) ∪ N (v)
  • 11. Item-based • Algorithm – For user u, get items set N(u) this user like before. – Recommend items which are similar to many items in N(u) to user u.
  • 12. Item-based CF pui = ∑ j∈S ( i , K ) ∩ N ( u ) w ji ruj N (i ) ∩ N ( j ) w ij = N (i ) ∪ N ( j )
  • 13. Item-based CF Why not use w ij = ? N (i ) ∩ N ( j ) N (i )
  • 14. Neighborhood-based • User-based vs. Item-based User-based Item-based Scalability Bad when user size is Bad when item size is large large Explanation Bad Good Novelty Bad Good Coverage Bad Good Cold start Bad for new users Bad for new items Performance Need to get many Only need to get users history current user’s history
  • 15. References • Amazon.com Recommendations item-to- item Collaborative Filtering. • Empirical Analysis of Predictive Algorithms for Collaborative Filtering.
  • 16. Graph-based • Users’ behaviors on items can be represented by bi-part graph. A 1 A 1 A 1 A 1 B 2 B 2 B 2 B 2 C 3 C 3 C 3 C 3 D 4 D 4 D 4 D 4
  • 17. Graph-based • Two nodes will have high relevance if – There are many paths in graph between two nodes. – Most of paths between two nodes is short. – Most paths do not go through nodes with high out-degree.
  • 18. Graph-based • Advantage – Heterogeneous data A 1 • Multiple user behaviors • Social Network B 2 • Context (Time, Location) C 3 • Disadvantage D 4 – Statistical-based – High cost for long path
  • 19. References • A Graph-based Recommender System for Digital Library. • Random-walk computation of similarities between nodes of a graph with application to collaborative recommendation.
  • 20. Latent Factor Model • Users and items are connect by latent features. A 1 a B 2 b C 3 c D 4
  • 21. Latent Factor Model rui = ∑ puk qik ˆ k Science Fiction 0.5 Science Fiction 0.9 Universe 0.9 Universe 0.9 Physical 0.8 Physical 0.5 Space Travel 0.8 Space Travel 0.7 Animation 0.3 Animation 0.1 Romance 0.0 Romance 0.0
  • 22. Latent Factor Model • How to get p, q? min ∑ (rui − ∑ puk qik ) + λ ( pu + qi ) 2 2 2 ( u ,i ) k = α (eui qik − λ puk ) puk + = α (eui puk − λ qik ) qik +
  • 23. Latent Factor Model • How to define rui – Rating prediction – Top-N recommendation • Implicit feedback data: only have positive samples and missing values, how to select negative samples?
  • 24. Latent Factor Model 1 (Sci-fi) 2 (Crime) 3 (Family) 4 (Horror) The Blair Witch The invisible Man Jaws 101 Dalmatians Project Frankenstein Back to the Meets the Wolf Lethal Weapon Pacific Heights Future Man Godzilla Total Recall Groundhog Day Stir of Echoes Star Wars VI Reservoir Dogs Tarzan Dead Calm The Terminator Donnie Brasco The Aristocats Phantasm The Jungle Book Alien The Fugitive Sleepy Hollow 2 Alien 2 La shou Shen tan Antz The Faculty
  • 25. Latent Factor Model • Advantage – High accuracy in rating prediction – Auto group items – Scalability is good – Learning-based • Disadvantage – Incremental updating – Real-time – Explanation
  • 26. References • http://www.informatik.uni- trier.de/~ley/db/indices/a- tree/k/Koren:Yehuda.html
  • 27. Cold Start • Problems – User cold start : new users – Item cold start : new items – System cold start : new systems
  • 28. User Cold Start • How to recommend items to new users? – Non-personalization recommendation • Most popular items • Highly Rated items – Using user register profile (Age, Gender, …)
  • 29. User Cold Start • Example: Gender and TV shows Data comes from IMDB : http://www.imdb.com/title/tt0412142/ratings
  • 30. User Cold Start Male Age : 20-30 Theoretical physicist Doctor American Irreligious
  • 31. How to get user interest quickly • When new user comes, his feedback on what items can help us better understand his interest? – Not very popular – Can represent a group of items – Users who like this item have different preference with users who dislike this item
  • 32. Item Cold Start • How to recommend new items to user? – Do not recommend How to recommend news??
  • 33. Item Cold Start • How to recommend new items to user? – Using content information Machine Data Mining Recommendation Learning
  • 34. System Cold Start • How to design recommender system when there is no user? – Pandora : Music Genome Project – Jinni : Movie Genome Project
  • 35. Architecture • Feature-based recommendation framework: A 1 a B 2 b C 3 c D 4 User Feature Item
  • 36. Architecture Male Scientist Physics
  • 37. Architecture • Advantage: – Heterogeneous data – Reasonable Explanation • Disadvantage: – Do not support user-based methods
  • 38. Open Questions • How to weight multiple behaviors? • How to improve diversity, novelty? • How to build feedback loop?