tangent : a novel, “surprise-me”, recommendation algorithm

Intelligent Database Systems Lab

國立雲林科技大學National Yunlin University of Science and Technology

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

Kensuke Onuma , Hanghang Tong , Christos Faloutsos2009.SIGKDD

Presented by Chien-Hao Kung2011/8/10


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Outlines· Motivation· Objectives· Methodology· Experiments· Conclusions· Comments


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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.


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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.


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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.

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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.

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

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Bridging Score (BRS)

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TANGENT Score Method 1: To use linear combination.

= Method 2: Skyline queries. Proposed Combination Method.

=

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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.

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=


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Synthetic Data Sets

Experiments

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=1.95


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MovieLens Data Set(Slapstick Movie Fan’ case )

Experiments

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MovieLens Data Set(Horror Movie Fan’ case )

Experiments

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MoveLens data setExperiments

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=0.08


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CIKM data setExperiments

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=0.22


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DBLP Data Set

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𝑆𝑢𝑟𝑝𝑟𝑖𝑠𝑖𝑛𝑔 𝐺𝑎𝑖𝑛 :𝑛𝑡𝑎𝑛−𝑛𝑟𝑒𝑙

𝑛𝑡𝑎𝑛

0.780.70


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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.


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Comments

· Advantages─ there are many pictures in this paper, so it can be

read intuitively

· application─ Information Storage and Retrieval

tangent : a novel, “surprise-me”, recommendation algorithm

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