User modeling for full-text federated search in peer-to-peer networks

@inproceedings{citeulike:2281691, abstract = {User modeling for information retrieval has mostly been studied to improve the effectiveness of information access in centralized repositories. In this paper we explore user modeling in the context of full-text federated search in peer-to-peer networks. Our approach models a user's persistent, long-term interests based on past queries, and uses the model to improve search efficiency for future queries that represent interests similar to past queries. Our approach also enables queries representing a user's transient, ad-hoc interests to be automatically recognized so that search for these queries can rely on a relatively large search radius to avoid sacrificing effectiveness for efficiency. Experimental results demonstrate that our approach can significantly improve the efficiency of full-text federated search without degrading its accuracy. Furthermore, the proposed approach does not require a large amount of training data, and is robust to a range of parameter values.}, address = {New York, NY, USA}, author = {Lu, Jie and Callan, Jamie }, booktitle = {SIGIR '06: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval}, citeulike-article-id = {2281691}, doi = {http://dx.doi.org/10.1145/1148170.1148229}, isbn = {1595933697}, pages = {332--339}, posted-at = {2008-03-17 03:36:19}, priority = {0}, publisher = {ACM}, title = {User modeling for full-text federated search in peer-to-peer networks}, url = {http://dx.doi.org/10.1145/1148170.1148229}, year = {2006} }

TY - CONF ID - citeulike:2281691 L3 - citeulike-article-id:2281691 TI - User modeling for full-text federated search in peer-to-peer networks T2 - SIGIR '06: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval SP - 332 EP - 339 AD - New York, NY, USA PB - ACM SN - 1595933697 N2 - User modeling for information retrieval has mostly been studied to improve the effectiveness of information access in centralized repositories. In this paper we explore user modeling in the context of full-text federated search in peer-to-peer networks. Our approach models a user's persistent, long-term interests based on past queries, and uses the model to improve search efficiency for future queries that represent interests similar to past queries. Our approach also enables queries representing a user's transient, ad-hoc interests to be automatically recognized so that search for these queries can rely on a relatively large search radius to avoid sacrificing effectiveness for efficiency. Experimental results demonstrate that our approach can significantly improve the efficiency of full-text federated search without degrading its accuracy. Furthermore, the proposed approach does not require a large amount of training data, and is robust to a range of parameter values. AU - Lu, Jie AU - Callan, Jamie PY - 2006/// UR - http://dx.doi.org/10.1145/1148170.1148229 ER -