1. TANGENT TANGENT A Novel, “Surprise-me”, Recommendation Algorithm Kensuke Onuma : Sony Corporation Hanghang Tong : Carnegie Mellon Univ. Christos Faloutsos : Carnegie Mellon Univ.

2. 2 Motivation Go off on a ‘TANGENT’ ! Movies Kevin Liz Tim Mark Jessica Mary John Rachel Bob Mike Tom Broadening users’ horizon More chance to increase sales of items

3. 3 What we want are … user movie comedy fans horror fans Conventional recommendation algorithms’ answer TANGENT’s answer A target user (= query node)

4. 4 Outline Motivation Problem definition Algorithm Experiments Conclusion

5. 5 Graphs for recommendation [bipartite graph] JohnMike ABCD MarkRachelTomMary EFGH : weighted based on rating : users and movies

6. 6 Problem definition of TANGENT Given: - An edge-weighted undirected graph with adjacency matrix - The set of query nodes Given: - An edge-weighted undirected graph with adjacency matrix - The set of query nodes Find: - A node that satisfy following conditions. (1) Close enough to (2) Possessing high potential to reach other nodes Find: - A node that satisfy following conditions. (1) Close enough to (2) Possessing high potential to reach other nodes user movie

7. 7 Outline Motivation Problem definition Algorithm Experiments Conclusion

8. 8 Outline of TANGENT algorithm 1.Calculate relevance score of each node to 2.Calculate bridging score of each node 3.Compute the TANGENT score by merging two criteria above user movie

9. 9 [Step 1] Relevance score Random walk with restart [Pan+ KDD ’04] 1 2 3 45 6 7 8 9 query node 10.577 20.132 30.123 4 50.036 60.001 70.006 80.001 9 Various Scalable Solution [Tong ’06] - OnTheFly - B_Lin - NB_Lin - BB_Lin (for bipartitle graph) Various Scalable Solution [Tong ’06] - OnTheFly - B_Lin - NB_Lin - BB_Lin (for bipartitle graph)

10. 10 [Step 2] Bridging score (Intuition) 1 23 4 5 76 1 2 3 4 5 7 6 a node in a group a node between groups ~0 smalllarge

11. 11 [Step 2] Bridging score (Detail) 1 23 4 neighbors

12. 12 [Step 3] TANGENT score A. Simple multiplication. (not linear combination, not skyline query, ) user movie query relevance score to query nodes relevance score among neighbors

13. 13 Example 1 2 3 45 6 7 8 9 node 10.5778.5794.949 20.1328.5791.129 30.12311.0851.362 40.12311.0851.362 50.03620.7890.755 60.0017.9670.010 70.00612.8470.074 80.0017.9670.010 90.0017.9670.010 query node Group 1Group 2

14. 14 Outline Motivation Problem definition Algorithm Experiments –Synthetic data –Real data MovieLens (user-movie) DBLP (author-paper) Conclusion on our paper

15. 15 Synthetic data [bipartite graph] 1 234 5 6789 101112 1314151617181920212223 242526 queryNo.1 in TANGENT node 1node 16 node 5node 20 node 12node 20

16. 16 Real data [MovieLens] User Preference (rating 5) - A Nightmare on Elm Street (1984) (Horror) - The Shining (1980) (Horror) - Jaws (1975) (Action, Horror) RankTitleGenre 1The Silence of the Lamb (1991)Dr, Thr 2Psycho (1960)Hor, Rom, Thr 3Pulp Fiction (1994)Cr, Dr 4An American Werewolf in London (1981) Hor 5Natural Born Killers (1994)Ac, Thr 6Carrie (1976)Hor 7Alien (1979)Ac, Hor, SF, Thr 8Twelve Monkeys (1995)Dr, SF 9Evil Dead II (1987)Ac, Ad, Com, Hor 10Scream (1996)Hor, Thr 15Star Wars (1977)Ac,Adv,Rom,SF,War 17Fargo (1996)Cr, Dr, Thr 22The Godfather (1972)Ac, Cr, Dr 45Contact (1997)Dr, SF RankTitleGenre 1The Silence of the Lambs (1991)Dr, Thr 2Scream (1996)Hor, Thr 3Pulp Fiction (1994)Cr, Dr 4Star Wars (1977)Ac, Adv, Rom, SF, War 5Fargo (1996)Cr, Dr, Thr 6Twelve Monkeys (1995)Dr, SF 7Psycho (1960)Hor, Rom, Thr 8The Godfather (1972)Ac, Cr, Dr 9Contact (1997)Dr, SF 10Alien (1979)Ac, Hor, SF, Thr 13An American Werewolf in London (1981) Hor 12Natural Born Killers (1994)Ac, Thr 16Carrie (1976)Hor 23Evil Dead II (1987)Ac, Ad, Com, Hor Ranked list by relevance score Ranked list by TANGENT score 943 users 1682 movies 55375 ratings

17. 17 RankTitleGenre 1The Flintstones (1994)Ch,Com 2Spy Hard (1996)Com 3Oliver & Company (1988)Ani,Chi 4Jack (1996)Com,Dr 5Son in Law (1993)Com 6Ace Ventura: When Nature Calls (1995) Com 7Renaissance Man (1994)Com,Dr,War 8Pocahontas (1995)Ani,Chi,Mus,Rom 9Corrina, Corrina (1994)Com,Dr,Rom 10Beverly Hillbillies, The (1993)Com 11Princess Bride, The (1987)Ac,Adv,Com,Rom 15Monty Python and the Holy Grail (1974) Com 21Empire Strikes Back, The (1980)Ac,Adv,Dr 26Raiders of the Lost Ark (1981)Ac,Adv 29Return of the Jedi (1983)Ac,Adv,Rom,SF,War 32Star Wars (1977)Ac,Adv,Rom,SF,War 42Toy Story (1995)Ani,Chi,Com 53Men in Black (1997)Com,Dr RankTitleGenre 1Star Wars (1977)Ac,Adv,Rom,SF,War 2Return of the Jedi (1983)Ac,Adv,Rom,SF,War 3The Princess Bride (1987)Ac,Adv,Com,Rom 4Toy Story (1995)Ani,Chi,Com 5Monty Python and the Holy Grail (1974) Com 6Spy Hard (1996)Com 7Raiders of the Lost Ark (1981)Ac,Adv 8Empire Strikes Back, The (1980)Ac,Adv,Dr 9Jack (1996)Com,Dr 10Men in Black (1997)Ac,Adv,Com,SF 25Ace Ventura: When Nature Calls (1995) Com 27Corrina, Corrina (1994)Com,Dr,Rom 35Son in Law (1993)Com 42Oliver & Company (1988)Ani,Chi 43Renaissance Man (1994)Com,Dr,War 52Pocahontas (1995)Ani,Chi,Mus,Rom 166The Beverly Hillbillies (1993)Com 1439The Flintstones (1994)Ch,Com relevance score TANGENT score User Preference (rating 5) - Robin Hood: Men in Tights (1993) (Comedy) - Young Frankenstein (1974) (Comedy, Horror) - Naked Gun 33 1/3: The Final Insult (1994) (Comedy) - Fatal Instinct (1993) (Comedy)

18. 18 Outline Motivation Problem definition Algorithm Experiments Conclusion

19. 19 Conclusion Definition of a novel recommendation problem –“how to make a recommendation that broadens the horizons of the user?” –[Approach] * close to the user preferences * have high connectivity to other groups Design of algorithm –“Relevance score” X “Bridging score” –Effective & Efficient Experiments –synthetic dataset –real dataset

20. 20 Thank you Kensuke Onuma Kensuke.Oonuma@jp.sony.com Hanghang Tong htong@cs.cmu.edu Christos Faloutsos christos@cs.cmu.edu Poster tonight ! 19:30 – 22:00 at Hôtel de Ville Code available http://www.cs.cmu.edu/~kensuke/