Cited By
View all- Santos RMacdonald COunis I(2015)Search Result DiversificationFoundations and Trends in Information Retrieval10.1561/15000000409:1(1-90)Online publication date: 1-Mar-2015
Leveraging integrated information to extract query subtopics for search result diversification
Authors: 
Wei Zheng, Hui Fang, Conglei Yao, Min WangAuthors Info & Claims
Abstract
Search result diversification aims to diversify search results to cover different query subtopics, i.e., pieces of relevant information. The state of the art diversification methods often explicitly model the diversity based on query subtopics, and their performance is closely related to the quality of subtopics. Most existing studies extracted query subtopics only from the unstructured data such as document collections. However, there exists a huge amount of information from structured data, which complements the information from the unstructured data. The structured data can provide valuable information about domain knowledge, but is currently under-utilized. In this article, we study how to leverage the integrated information from both structured and unstructured data to extract high quality subtopics for search result diversification. We first discuss how to extract subtopics from structured data. We then propose three methods to integrate structured and unstructured data. Specifically, the first method uses the structured data to guide the subtopic extraction from unstructured data, the second one uses the unstructured data to guide the extraction, and the last one first extracts the subtopics separately from two data sources and then combines those subtopics. Experimental results in both Enterprise and Web search domains show that the proposed methods are effective in extracting high quality subtopics from the integrated information, which can lead to better diversification performance.
References
[1]
Agrawal, R., Gollapudi, S., Halverson, A., & Leong, S. (2009). Diversifying search results. In Proceedings of WSDM’09.
[2]
Balog, K., Bron, M., He, J., Hofmann, K., Meij, E., de Rijke, M., et al. (2009a). The University of Amesterdam at TREC 2009. In Proceedings of TREC’09.
[3]
Balog, K., de Vries, A. P., Serdyukov, P., Thomas, P., & Westerveld, T. (2009b). Overview of the TREC 2009 entity track. In Proceedings of TREC’09.
[4]
Bi, W., Yu, X., Liu, Y., Guan, F., Peng, Z., Xu, H., et al. (2009). ICTNET at web track 2009 diversity task. In Proceedings of TREC’09.
[5]
Carbonell, J., & Goldstein, J. (1998). The use of MMR, diversity-based reranking for reordering documents and producing summaries. In Proceedings of SIGIR’98.
[6]
Carterette, B., & Chandar, P. (2009). Probabilistic models of novel document rankings for faceted topic retrieval. In Proceedings of CIKM’09.
[7]
Chapelle, O., Metzzler, D., Zhang, Y., & Grinspan, P. (2009). Expected reciprocal rank for graded relevance. In Proceedings of CIKM’09.
[8]
Chen, Z., & Li, T. (2007). Addressing diverse user preferences in sql-query-result navigation. In Proceedings of SIGMOD’07.
[9]
Clarke, C. L. A., Craswell, N., & Soboroff, I. (2009a). Overview of the TREC 2009 web track. In Proceedings of TREC’09.
[10]
Clarke, C. L. A., Craswell, N., Soboroff, I., & Cormack, G. V. (2010). Overview of the TREC 2010 web track. In Proceedings of TREC’10.
[11]
Clarke, C. L. A., Koll, M., & Vechtomova, O. (2009b). An effectiveness measure for ambiguous and underspecified queries. In Proceedings of ICTIR’09.
[12]
Clarke, C. L. A., Kolla, M., Cormack, G. V., Vechtomova, O., Ashkann, A., Buttcher, S., et al. (2008). Novelty and diversity in information retrieval evaluation. In Proceedings of SIGIR’08.
[13]
Dang, V., Xue, X., & Croft, W. B. (2011). Inferring query aspects from reformulations using clustering. In Proceedings of NTCIR-9.
[14]
Demidova, E., Fankhauser, P., Zhou, X., & Nejdl, W. (2010). Divq: Diversification for keyword search over structured databases. In Proceedings of SIGIR’10.
[15]
Dou, Z., Chen, K., Song, R., Ma, Y., Shi, S., & Wen, J.-R. (2009). Microsoft research Asia at the web track of TREC 2009. In Proceedings of TREC’09.
[16]
Dou, Z., Hu, S., Chen, K., Song, R., & Wen, J. R. (2011). Multi-dimensional search result diversification. In Proceedings of WSDM’11.
[17]
Fang, H., & Zhai, C. (2006). Semantic term matching in axiomatic approaches to information retrieval. In Proceedings of SIGIR’06.
[18]
Hauff, C., & Hiemstra, D. (2009). University of Twente @ TREC 2009: Indexing half a billion web pages. In Proceedings of TREC’09.
[19]
Hawking, D. (2004). Challenges in enterprise search. In Proceedings of ADC’04.
[20]
He J., Meij E., and de Rijke M. Result diversification based on query-specific cluster ranking Journal of the American Society for Information Science and Technology 2010 62 3 550-571
[21]
Li, Z., Cheng, F., Xiang, Q., Miao, J., Xue, Y., Zhu, T., et al. (2009). THUIR at TREC 2009 web track: Finding relevant and diverse results for large scale web search. In Proceedings of TREC’09.
[22]
Lubell-Doughtie, P., & Hofmann, K. (2011). Improving result diversity using probabilistic latent semantic analysis. In Proceedings of DIR’11.
[23]
Mei, Q., Ling, X., Wondra, M., Su, H., & Zhai, C. (2007). Topic sentiment mixture: Modeling facets and opinions in weblogs. In Proceedings of WWW’07.
[24]
Radlinski, F., & Dumais, S. T. (2006). Improving personalized web search using result diversification. In Proceedings of SIGIR’06.
[25]
Radlinski, F., Szummer, M., & Craswell, N. (2010). Inferring query intent from reformulations and clicks. In Proceedings of WWW’10.
[26]
Sakai, T., & Song, R. (2012). Diversified search evaluation: Lessons from the NTCIR-9 INTENT task. Information Retrieval.
[27]
Santos, R. L. T., Macdonald, C., & Ounis, I. (2010a). Exploiting query reformulations for web search result diversification. In Proceedings of WWW’10.
[28]
Santos, R. L. T., Macdonald, C., & Ounis, I. (2010b). Selectively diversifying web search results. In Proceedings of CIKM’10.
[29]
Santos, R. L. T., Peng, J., Macdonald, C., & Ounis, I. (2010c). Explicit search result diversification through sub-queries. In Proceedings of ECIR’10.
[30]
Song, R., Zhang, M., Sakai, T., Kato, M. P., Liu, Y., Sugimoto, M., et al. (2011). Overview of the ntcir-9 intent task. In Proceedings of CIKM’11.
[31]
van Rijsbergen C.J. Information Retrieval 1979 Strand, London Butterworths
[32]
Vee, E., Srivastava, U., Shanmugasundaram, J., Bhat, P., & Yahia, S. A. (2008). Efficient computation of diverse query results. In Proceedings of ICDE’08.
[33]
Zhai, C., Cohen, W., & Lafferty, J. (2003). Beyond independent relevance: Methods and evaluation metrics for subtopic retrieval. In Proceedings of SIGIR’03.
[34]
Zhai, C., & Lafferty, J. (2001). A study of smoothing methods for language models applied to ad hoc information retrieval. In Proceedings of SIGIR’01.
[35]
Zhang, S., Lu, K., & Wang, B. (2011). ICTIR subtopic mining system at NTCIR-9 INTENT task. In Proceedings of NTCIR-9.
[36]
Zheng, W., Fang, H., Yao, C.,& Wang, M. (2011a). Search result diversification for enterprise search. In Proceedings of CIKM’11.
[37]
Zheng, W., Wang, X., Fang, H., & Cheng, H. (2011b). An exploration of pattern-based subtopic modeling for search result diversification. In Proceedings of JCDL’11.
[38]
Zheng W., Wang X., Fang H., and Cheng H. Coverage-based search result diversification Information Retrieval 2012 15 5 433-457
Index Terms
- Leveraging integrated information to extract query subtopics for search result diversification
Index terms have been assigned to the content through auto-classification.
Recommendations
Search result diversification for enterprise data
CIKM '11: Proceedings of the 20th ACM international conference on Information and knowledge managementSearch result diversification aims to return a list of diversified relevant documents in order to satisfy different user information needs. Most of the efforts focused on Web Search, and few studies have considered another important search domain, i.e., ...
An exploration of pattern-based subtopic modeling for search result diversification
JCDL '11: Proceedings of the 11th annual international ACM/IEEE joint conference on Digital librariesTraditional information retrieval models do not necessarily provide users with optimal search experience because the top ranked documents may contain the same piece of relevant information, i.e., the same subtopic of a query. The goal of search result ...
Term level search result diversification
SIGIR '13: Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrievalCurrent approaches for search result diversification have been categorized as either implicit or explicit. The implicit approach assumes each document represents its own topic, and promotes diversity by selecting documents for different topics based on ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
© Springer Science+Business Media New York 2013.
Publisher
Kluwer Academic Publishers
United States
Publication History
Published: 01 February 2014
Accepted: 21 February 2013
Received: 08 October 2012
Author Tags
Qualifiers
- Research-article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 08 Feb 2025
Other Metrics
Citations
Cited By
View all- Santos RMacdonald COunis I(2015)Search Result DiversificationFoundations and Trends in Information Retrieval10.1561/15000000409:1(1-90)Online publication date: 1-Mar-2015