TANGENT A Novel, “Surprise-me”, Recommendation Algorithm

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

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

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

Outline • Motivation • Problem definition • Algorithm • Experiments • Conclusion

Graphs for recommendation[bipartite graph] Mark Rachel Tom Mary John Mike A B C D E F G H : users and movies : weighted based on rating

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

Outline of TANGENT algorithm • Calculate relevance score of each node to • Calculate bridging score of each node • Compute the TANGENT scoreby merging two criteria above user movie

[Step 1] Relevance score Random walk with restart [Pan+ KDD ’04] 2 4 5 7 9 query node 1 3 6 8 Various Scalable Solution [Tong ’06] - OnTheFly - B_Lin - NB_Lin - BB_Lin (for bipartitle graph)

[Step 2] Bridging score (Intuition) a node in a group a node between groups 2 3 2 5 4 1 6 1 5 3 7 4 7 6 ~0 ~0 small large

[Step 2] Bridging score (Detail) 2 3 neighbors 1 4

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

Example 2 4 5 7 9 query node 1 3 6 8 Group 1 Group 2

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

Synthetic data[bipartite graph] 1 5 2 3 4 6 7 8 9 13 14 15 16 17 18 19 20 21 22 23 10 11 12 24 25 26

Real data [MovieLens] • User Preference (rating 5) • - A Nightmare on Elm Street (1984) (Horror) • The Shining (1980) (Horror) • Jaws (1975) (Action, Horror) 943 users 1682 movies 55375 ratings Ranked list by TANGENT score Ranked list by relevance 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) TANGENT score relevance score

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

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

TANGENT A Novel, “Surprise-me”, Recommendation Algorithm