Active feedback in ad hoc information retrieval Export

@inproceedings{citeulike:1714903, abstract = {Information retrieval is, in general, an iterative search process, in which the user often has several interactions with a retrieval system for an information need. The retrieval system can actively probe a user with questions to clarify the information need instead of just passively responding to user queries. A basic question is thus how a retrieval system should propose questions to the user so that it can obtain maximum benefits from the feedback on these questions. In this paper, we study how a retrieval system can perform active feedback, i.e., how to choose documents for relevance feedback so that the system can learn most from the feedback information. We present a general framework for such an active feedback problem, and derive several practical algorithms as special cases. Empirical evaluation of these algorithms shows that the performance of traditional relevance feedback (presenting the top K documents) is consistently worse than that of presenting documents with more diversity. With a diversity-based selection algorithm, we obtain fewer relevant documents, however, these fewer documents have more learning benefits.}, address = {New York, NY, USA}, author = {Shen, Xuehua and Zhai, ChengXiang}, booktitle = {SIGIR '05: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval}, citeulike-article-id = {1714903}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1076047}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/1076034.1076047}, doi = {10.1145/1076034.1076047}, isbn = {1-59593-034-5}, keywords = {plurality, relevancefeedback}, location = {Salvador, Brazil}, pages = {59--66}, posted-at = {2008-05-12 19:03:39}, priority = {2}, publisher = {ACM}, title = {Active feedback in ad hoc information retrieval}, url = {http://dx.doi.org/10.1145/1076034.1076047}, year = {2005} }

TY - CONF ID - citeulike:1714903 L3 - citeulike-article-id:1714903 N2 - Information retrieval is, in general, an iterative search process, in which the user often has several interactions with a retrieval system for an information need. The retrieval system can actively probe a user with questions to clarify the information need instead of just passively responding to user queries. A basic question is thus how a retrieval system should propose questions to the user so that it can obtain maximum benefits from the feedback on these questions. In this paper, we study how a retrieval system can perform active feedback, i.e., how to choose documents for relevance feedback so that the system can learn most from the feedback information. We present a general framework for such an active feedback problem, and derive several practical algorithms as special cases. Empirical evaluation of these algorithms shows that the performance of traditional relevance feedback (presenting the top K documents) is consistently worse than that of presenting documents with more diversity. With a diversity-based selection algorithm, we obtain fewer relevant documents, however, these fewer documents have more learning benefits. CY - Salvador, Brazil SN - 1-59593-034-5 AD - New York, NY, USA EP - 66 TI - Active feedback in ad hoc information retrieval PB - ACM T2 - SIGIR '05: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval SP - 59 KW - plurality KW - relevancefeedback AU - Shen, Xuehua AU - Zhai, ChengXiang PY - 2005/// UR - http://dx.doi.org/10.1145/1076034.1076047 ER -