Intelligent Database Systems Lab 國立雲林科技大學 National Yunlin University of Science and Technology 1 TANGENT: A Novel, “Surprise-me”, Recommendation Algorithm Kensuke Onuma, Hanghang Tong, Christos Faloutsos 2009.SIGKDD Presented by Chien-Hao Kung 2011/8/10

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 2 Outlines  Motivation  Objectives  Methodology  Experiments  Conclusions  Comments

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 3 Motivation  Most of the recommendation algorithms focus on the precision in the proximity to user preferences. However, this strategy tends to suggest items only on the center of user preferences and thus narrows down the users’ horizons.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 4 Objectives  To propose a method which are well connected to 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.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology 5

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology 6

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology Relevance Score (RS) It’s proposed to use random walk with restart. 7

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology Bridging Score (BRS) 8

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology 9

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology Scalability Computing Relevance Score Using random walk with restart Computing Bridging Score The R can be re-used in computing bridging score It doesn’t need to compute bridging scores of user nodes for recommendation. Merging It needs just a multiplication for each of the n- q=O(n) candidate nodes. 10

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Synthetic Data Sets Experiments 11

Intelligent Database Systems Lab N.Y.U.S.T. I. M. MovieLens Data Set(Slapstick Movie Fan’ case ) Experiments 12

Intelligent Database Systems Lab N.Y.U.S.T. I. M. MovieLens Data Set(Horror Movie Fan’ case ) Experiments 13

Intelligent Database Systems Lab N.Y.U.S.T. I. M. MoveLens data set Experiments 14

Intelligent Database Systems Lab N.Y.U.S.T. I. M. CIKM data set Experiments 15

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments DBLP Data Set

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 17 Conclusions  It’s proposed TANGENT algorithm to find items that are close to the user preferences, while they also have high connectivity to other groups.  Careful design decisions, so that the resulting method is (a) parameter-free (b) effective and (c) fast.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 18 Comments  Advantages ─ there are many pictures in this paper, so it can be read intuitively  application ─ Information Storage and Retrieval