The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
Exploiting query reformulations for web search result diversification
Abstract
When a Web user's underlying information need is not clearly specified from the initial query, an effective approach is to diversify the results retrieved for this query. In this paper, we introduce a novel probabilistic framework for Web search result diversification, which explicitly accounts for the various aspects associated to an underspecified query. In particular, we diversify a document ranking by estimating how well a given document satisfies each uncovered aspect and the extent to which different aspects are satisfied by the ranking as a whole. We thoroughly evaluate our framework in the context of the diversity task of the TREC 2009 Web track. Moreover, we exploit query reformulations provided by three major Web search engines (WSEs) as a means to uncover different query aspects. The results attest the effectiveness of our framework when compared to state-of-the-art diversification approaches in the literature. Additionally, by simulating an upper-bound query reformulation mechanism from official TREC data, we draw useful insights regarding the effectiveness of the query reformulations generated by the different WSEs in promoting diversity.
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Cites
Cumulated gain-based evaluation of IR techniques
Järvelin, K., & Kekäläinen, J. (2002) · ACM Transactions on Information Systems · doi:10.1145/582415.582418 · Cited by 4,841
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The use of MMR, diversity-based reranking for reordering documents and producing summaries
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The Use of MMR, Diversity-Based Reranking for Reordering Documents and Producing Summaries
Carbonell, J., & Goldstein, J. (2017) · ACM SIGIR Forum · doi:10.1145/3130348.3130369 · Cited by 2,156
THE PROBABILITY RANKING PRINCIPLE IN IR
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Novelty and diversity in information retrieval evaluation
Clarke, C. L. A., Kolla, M., Cormack, G. V., Vechtomova, O., Ashkan, A., Büttcher, S., & MacKinnon, I. (2008) · Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval · doi:10.1145/1390334.1390446 · Cited by 962
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Cited by
Diversity in Big Data: A Review
Drosou, M., Jagadish, H. V., Pitoura, E., & Stoyanovich, J. (2017) · Big Data · doi:10.1089/big.2016.0054 · Cited by 140
Coverage-based search result diversification
Zheng, W., Wang, X., Fang, H., & Cheng, H. (2012) · Information Retrieval · doi:10.1007/s10791-011-9178-4 · Cited by 16
Metadata
- Authors
- Santos, R. L. T., Macdonald, C., & Ounis, I.
- Venue
- Proceedings of the 19th international conference on World wide web
- Publisher
- ACM
- Published
- 2010-04-26
- Pages
- 881-890
- OpenAlex
- W2023188792
- Cited by
- 441
- References
- 28
- FWCI
- 24.51
- Type
- conference paper
- Language
- en
- Open access
- closed
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