TANGENT: a novel, 'Surprise me', recommendation algorithm

Most of recommender systems try to find items that are most relevant to the older choices of a given user. Here we focus on the "surprise me" query: A user may be bored with his/her usual genre of items (e.g., books, movies, hobbies), and may want a recommendation that is related, but off the beaten path, possibly leading to a new genre of books/movies/hobbies. How would we define, as well as automate, this seemingly selfcontradicting request? We introduce TANGENT, a novel recommendation algorithm to solve this problem. The main idea behind TANGENT is to envision the problem as node selection on a graph, giving high scores to nodes that are well connected to the older choices, and at the same time well connected to unrelated choices. The method is carefully designed to be (a) parameter-free (b) effective and (c) fast. We illustrate the benefits of TANGENT with experiments on both synthetic and real data sets. We show that TANGENT makes reasonable, yet surprising, horizon-broadening recommendations. Moreover, it is fast and scalable, since it can easily use existing fast algorithms on graph node proximity.

[1]  Jon M. Kleinberg,et al.  Group formation in large social networks: membership, growth, and evolution , 2006, KDD '06.

[2]  Jiawei Han,et al.  Mining Compressed Frequent-Pattern Sets , 2005, VLDB.

[3]  U. Brandes.  A faster algorithm for betweenness centrality , 2001 .

[4]  Lise Getoor,et al.  Effective label acquisition for collective classification , 2008, KDD.

[5]  John Riedl,et al.  GroupLens: an open architecture for collaborative filtering of netnews , 1994, CSCW '94.

[6]  Jure Leskovec,et al.  Statistical properties of community structure in large social and information networks , 2008, WWW.

[7]  Leonard M. Freeman,et al.  A set of measures of centrality based upon betweenness , 1977 .

[8]  Fei Wang,et al.  Recommendation on Item Graphs , 2006, Sixth International Conference on Data Mining (ICDM'06).

[9]  Christos Faloutsos,et al.  Random walk with restart: fast solutions and applications , 2008, Knowledge and Information Systems.

[10]  Peter G. Doyle,et al.  Random Walks and Electric Networks: REFERENCES , 1987 .

[11]  Jian Pei,et al.  Computing Compressed Multidimensional Skyline Cubes Efficiently , 2007, 2007 IEEE 23rd International Conference on Data Engineering.

[12]  G. Karypis,et al.  Multilevel k-way hypergraph partitioning , 1999, Proceedings 1999 Design Automation Conference (Cat. No. 99CH36361).

[13]  François Fouss,et al.  Random-Walk Computation of Similarities between Nodes of a Graph with Application to Collaborative Recommendation , 2007, IEEE Transactions on Knowledge and Data Engineering.

[14]  Mark E. J. Newman.  A measure of betweenness centrality based on random walks , 2005, Soc. Networks.

[15]  Sean M. McNee,et al.  Being accurate is not enough: how accuracy metrics have hurt recommender systems , 2006, CHI Extended Abstracts.

[16]  John Riedl,et al.  Recommender Systems for Large-scale E-Commerce : Scalable Neighborhood Formation Using Clustering , 2002 .

[17]  Srinivasan Parthasarathy,et al.  Discovering frequent topological structures from graph datasets , 2005, KDD '05.

[18]  Diane J. Cook,et al.  Graph-based anomaly detection , 2003, KDD '03.

[19]  Yehuda Koren,et al.  Modeling relationships at multiple scales to improve accuracy of large recommender systems , 2007, KDD '07.

[20]  Mark E. J. Newman,et al.  The Structure and Function of Complex Networks , 2003, SIAM Rev..

[21]  Yehuda Koren,et al.  Measuring and extracting proximity in networks , 2006, KDD '06.

[22]  Christos Faloutsos,et al.  Center-piece subgraphs: problem definition and fast solutions , 2006, KDD '06.

[23]  Jon M. Kleinberg,et al.  Inferring Web communities from link topology , 1998, HYPERTEXT '98.

[24]  Rajeev Motwani,et al.  The PageRank Citation Ranking : Bringing Order to the Web , 1999, WWW 1999.

[25]  M E J Newman,et al.  Community structure in social and biological networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[26]  Jimeng Sun,et al.  Relevance search and anomaly detection in bipartite graphs , 2005, SKDD.

[27]  Sanjay Ghemawat,et al.  MapReduce: Simplified Data Processing on Large Clusters , 2004, OSDI.

[28]  Albert-László Barabási,et al.  Internet: Diameter of the World-Wide Web , 1999, Nature.

[29]  Hideo Miyahara,et al.  A Community-Based Recommendation System to Reveal Unexpected Interests , 2005, 11th International Multimedia Modelling Conference.

[30]  Christos Faloutsos,et al.  Electricity Based External Similarity of Categorical Attributes , 2003, PAKDD.

[31]  Jennifer Neville,et al.  Using relational knowledge discovery to prevent securities fraud , 2005, KDD '05.

[32]  Taher H. Haveliwala.  Topic-sensitive PageRank , 2002, IEEE Trans. Knowl. Data Eng..

[33]  Christos Faloutsos,et al.  Graphs over time: densification laws, shrinking diameters and possible explanations , 2005, KDD '05.