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  4. Mining subtopics from different aspects for diversifying search results
 
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Mining subtopics from different aspects for diversifying search results

Journal
Information Retrieval
Journal Volume
16
Journal Issue
4
Pages
452-483
Date Issued
2013
Author(s)
Wang C.-J.
Lin Y.-W.
Tsai M.-F.
Chen H.-H.  
DOI
10.1007/s10791-012-9215-y
URI
https://scholars.lib.ntu.edu.tw/handle/123456789/413135
URL
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84880808899&doi=10.1007%2fs10791-012-9215-y&partnerID=40&md5=5827abcecb65ea4b1a47102cdcda9e2b
Abstract
User queries to the Web tend to have more than one interpretation due to their ambiguity and other characteristics. How to diversify the ranking results to meet users' various potential information needs has attracted considerable attention recently. This paper is aimed at mining the subtopics of a query either indirectly from the returned results of retrieval systems or directly from the query itself to diversify the search results. For the indirect subtopic mining approach, clustering the retrieval results and summarizing the content of clusters is investigated. In addition, labeling topic categories and concept tags on each returned document is explored. For the direct subtopic mining approach, several external resources, such as Wikipedia, Open Directory Project, search query logs, and the related search services of search engines, are consulted. Furthermore, we propose a diversified retrieval model to rank documents with respect to the mined subtopics for balancing relevance and diversity. Experiments are conducted on the ClueWeb09 dataset with the topics of the TREC09 and TREC10 Web Track diversity tasks. Experimental results show that the proposed subtopic-based diversification algorithm significantly outperforms the state-of-the-art models in the TREC09 and TREC10 Web Track diversity tasks. The best performance our proposed algorithm achieves is £\-nDCG@5 0.307, IA-P@5 0.121, and £\#-nDCG@5 0.214 on the TREC09, as well as £\-nDCG@10 0.421, IA-P@10 0.201, and £\#-nDCG@10 0.311 on the TREC10. The results conclude that the subtopic mining technique with the up-to-date users' search query logs is the most effective way to generate the subtopics of a query, and the proposed subtopic-based diversification algorithm can select the documents covering various subtopics. ? 2012 Springer Science+Business Media New York.
Subjects
Diversified retrieval
Search result re-ranking
Subtopic mining
SDGs

[SDGs]SDG13

Type
journal article

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