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Improving Web Search Results Using Affinity Graph

Improving Web Search Results Using Affinity Graph. Benyu Zhang, Hua Li, Yi Liu, Lei Ji, Wensi Xi, Weiguo Fan, Zheng Chen, Wei-Ying Ma Microsoft Research Asia SIGIR 2005. INTRODUCTION. The top search results can hardly cover a sufficient variety of topics (redundant)

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Improving Web Search Results Using Affinity Graph

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  1. Improving Web Search Results Using Affinity Graph Benyu Zhang, Hua Li, Yi Liu, Lei Ji, Wensi Xi, Weiguo Fan, Zheng Chen, Wei-Ying Ma Microsoft Research Asia SIGIR 2005

  2. INTRODUCTION • The top search results can hardly cover a sufficient variety of topics (redundant) • re-ranking method based on MMR • There is no indication about how informative a returned document is on the query topic (coverage) • subtopic retrieval method • two novel metrics, diversity and information richness

  3. BACKGROUND • The most famous works on link analysis • PageRank and HITS algorithm • Explicit link analysis and implicit link analysis • two web pages are implicitly linked if they are visited sequentially by the same end-user. • DirectHit and Small Web Search

  4. AFFINITY RANKING

  5. AFFINITY RANKING • Diversity: Given a set of documents R , we use diversity Div(R) to denote the number of different topics contained in R. • Information Richness: Given a document collection D={d1…dn}, we use information richness InfoRich (di) to denote richness of information contained in the document di with respect to the entire collection D.

  6. Affinity Graph Construction • According to vector space model , similarity between a documents pair of di and dj can be calculated as • For further measurement on the significance of the similarity between each document pair, we define the affinity of dj to di as

  7. InformationRichness Computation • After obtaining Affinity Graph, we apply a link analysis algorithm similar to PageRank • M is normalized to make the sum of each row equal to 1.

  8. InformationRichness Computation • the score of document di can be deduced from those of all other document linked to it • With dumping factor c (similar to the random jumping factor in PageRank):

  9. InformationRichness Computation • information can choose where to flow according to the following two rules: • With a probability c, the information will flow into document nodes which di links • With a probability of c - 1 the information will randomly flow into any document in the collection.

  10. Diversity Penalty

  11. Re-ranking Method • The re-ranking mechanism is a combination of results from fulltext search and Affinity Ranking • score-combination

  12. Re-ranking Method • rank-combination

  13. EXPERIMENTS • Yahoo! Directory • contained a total of 292,216 categories (including leaf categories and non-leaf categories) • All categories are organized into a 16-level hierarchy. • we have downloaded 792,601 documents in total. • ODP (Open Directory Project) • We downloaded the directory in August, 2004. ODP includes a total of 172,565 categories • we have downloaded 1,547,000 documents in total.

  14. EXPERIMENTS • Newsgroup dataset • The Newsgroup data is composed of 256,449 posts collected from 117 commercial application with a total size of about 400M • Title and content of the post are given a 3:1 weighting ratio in indexing process • There is no explicit link existing among the posts • large amount of posts are very likely to be devoted to the same topic

  15. Affinity Ranking vs. K-Means Clustering

  16. Affinity Ranking vs. K-Means Clustering • The top 1000 search results of each query are passed to AR or Kmeans algorithm to re-rank top 10 results • For K-Means algorithm, we set K=10 and use the top 1 document of each cluster to construct the top 10 results

  17. Affinity Ranking vs. K-Means Clustering

  18. Affinity Ranking in Newsgroup dataset • Query • We compare our approach with the Okapi system in three aspects: diversity, information richness and relevance

  19. Affinity Ranking in Newsgroup dataset • Four researchers are hired to labele the top 50 search results for each of the 20 queries based on the following steps:

  20. Affinity Ranking in Newsgroup dataset • N is the number of users • X could be diversity, information richness, or relevance of the top search results • A and F represent results from our ranking scheme and full-text search

  21. Improvement in Top 10 Search Results • As the top 10 search results always receive the most attention of end-users • In this experiment, we use the rank-combination scheme and which α= 0 and β =1

  22. Improvement within Top 50 Search Results

  23. Improvement within Top 50 Search Results

  24. A Case Study • This example is extracted from our experiments on the Newsgroup search for the query “Outlook print error”

  25. CONCLUSIONS • Proposed two new metrics, diversity and information richness • A novel ranking scheme, Affinity Ranking, is proposed to re-rank the search results • Our experiments showed that the proposed metrics and new ranking method can effectively improve the search performance • Future work includes scaling our Affinity Ranking computation, for example, to the Web scale

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