SIGIR 1Intelligent Database Systems Lab

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

Improving Web Search Results Using Affinity Graph

Advisor : Dr. HsuPresenter : Jia-Hao YangAuthor :Benyu Zhang , Hua Li , Yi Liu , Wensi Xi , Weiguo Fan

SIGIR2Intelligent Database Systems Lab

N.Y.U.S.T.

I. M.Outline Motivation Objective Definition Methods (Affinity Ranking) Experiments Conclusion Opinion

SIGIR3Intelligent Database Systems Lab

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I. M.Motivation situation

─ Many of the queries are ambiguous. ─ the user’s information needs are unknown.

Ex : “ 足球” , 是只想要足球還是要找足球賽 In traditional, precision and recall are two metr

ics, but these didn’t consider the content of documents.

Hyperlink

SIGIR4Intelligent Database Systems Lab

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I. M.Objective Two metrics, diversity and information

richness, have been proposed to improve this problem.

Re-ranking the top search results to satisfy the user’s information needs.

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I. M.Definition Diversity measures the variety of topics in a gr

oup of documents. Information richness measures how many dif

ferent topics a single document contains.

SIGIR6Intelligent Database Systems Lab

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I. M.Methods AG : According to vector space model, each

document can be represented ,

If we consider documents as nodes, the document collection can be modeled

as a graph by generating the link between

documents.

d1

d5d6

d4

d3d2

SIGIR7Intelligent Database Systems Lab

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I. M.Methods(cont.) Information richness : 1st

2nd

SIGIR8Intelligent Database Systems Lab

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I. M.Methods(cont.) Diversity penalty : 1st :

2nd

3rd ,

4th

5th 2nd

Re-ranking :─ The score-combination scheme uses a linear combination of two parts:

─ The rank-combination scheme of re-ranking uses a linear combination of the ranks based on full-text search and Affinity Ranking :

SIGIR9Intelligent Database Systems Lab

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I. M.Experiments (In Yahoo & ODP) Affinity Ranking vs. K-Means Clustering

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I. M.Experiments (cont.)

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I. M.Experiments (cont.)

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I. M.Experiments (In Newsgroup)

Improve in Top 10 Search Results : As the top 10 search results always receive the most attention of end-users,

we show how Affinity Ranking affects the top 10 search results from the newsgroup data set.

SIGIR13Intelligent Database Systems Lab

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I. M.Experiments (cont.) Improve within Top 50 Search Results

SIGIR14Intelligent Database Systems Lab

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I. M.Experiments (cont.)

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I. M.Experiments (α & β)

SIGIR16Intelligent Database Systems Lab

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I. M.A Case Study Outlook print error :

SIGIR17Intelligent Database Systems Lab

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I. M.Conclusion This paper proposed two new metrics, diversity and

information richness, and a novel ranking scheme, Affinity Ranking, to measure the search performance.

By presenting wider topic coverage and more highly informative results in each topic in the top results, this method can effectively improve the search performance.

SIGIR18Intelligent Database Systems Lab

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I. M.Opinion Future work : scaling the AR computation, to

the Web scale.