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

TANGENT : A Novel, “Surprise-me”, Recommendation Algorithm. Kensuke Onuma , Hanghang Tong , Christos Faloutsos 2009.SIGKDD Presented by Chien-Hao Kung 2011/8/10. Outlines. Motivation Objectives Methodology Experiments Conclusions Comments. Motivation.

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

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  1. TANGENT: A Novel, “Surprise-me”, Recommendation Algorithm Kensuke Onuma , Hanghang Tong , Christos Faloutsos 2009.SIGKDD Presented by Chien-Hao Kung 2011/8/10

  2. Outlines • Motivation • Objectives • Methodology • Experiments • Conclusions • Comments

  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.

  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.

  5. Methodology • Define TANGENT problem as follows: • Given: an edge-weighted undirected graph G with adjacency matrix A, the set of query nodes Q=()1i<k. • Find: a node that (1) is close enough to Q, and (2) has high potential to reach other nodes.

  6. Methodology • Framework of TANGENT Algorithm • Step1: Calculate relevance score (RS) of each node: • Step2: Calculate Bridging Score (BRS) of each node: • Step3:Compute the TANGENT score () by somehow merging two criteria above.

  7. Methodology • Relevance Score (RS) • It’s proposed to use random walk with restart.

  8. Methodology • Bridging Score (BRS)

  9. Methodology • TANGENT Score • Method 1: To use linear combination. • = • Method 2: Skyline queries. • Proposed Combination Method. • =

  10. 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.

  11. Experiments =1.95 Synthetic Data Sets

  12. Experiments MovieLens Data Set(Slapstick Movie Fan’ case )

  13. Experiments MovieLens Data Set(Horror Movie Fan’ case )

  14. Experiments =0.08 MoveLens data set

  15. Experiments =0.22 CIKM data set

  16. Experiments 0.78 0.70 DBLP Data Set

  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.

  18. Comments • Advantages • there are many pictures in this paper, so it can be read intuitively • application • Information Storage and Retrieval

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