%0 Conference Paper %1 citeulike:11093758 %A Bennett, Paul N. %A White, Ryen W. %A Chu, Wei %A Dumais, Susan T. %A Bailey, Peter %A Borisyuk, Fedor %A Cui, Xiaoyuan %B Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval %C New York, NY, USA %D 2012 %I ACM %K adaptive-search %P 185--194 %R 10.1145/2348283.2348312 %T Modeling the Impact of Short- and Long-term Behavior on Search Personalization %U http://dx.doi.org/10.1145/2348283.2348312 %X User behavior provides many cues to improve the relevance of search results through personalization. One aspect of user behavior that provides especially strong signals for delivering better relevance is an individual's history of queries and clicked documents. Previous studies have explored how short-term behavior or long-term behavior can be predictive of relevance. Ours is the first study to assess how short-term (session) behavior and long-term (historic) behavior interact, and how each may be used in isolation or in combination to optimally contribute to gains in relevance through search personalization. Our key findings include: historic behavior provides substantial benefits at the start of a search session; short-term session behavior contributes the majority of gains in an extended search session; and the combination of session and historic behavior out-performs using either alone. We also characterize how the relative contribution of each model changes throughout the duration of a session. Our findings have implications for the design of search systems that leverage user behavior to personalize the search experience. %@ 978-1-4503-1472-5

@inproceedings{citeulike:11093758, abstract = {{User behavior provides many cues to improve the relevance of search results through personalization. One aspect of user behavior that provides especially strong signals for delivering better relevance is an individual's history of queries and clicked documents. Previous studies have explored how short-term behavior or long-term behavior can be predictive of relevance. Ours is the first study to assess how short-term (session) behavior and long-term (historic) behavior interact, and how each may be used in isolation or in combination to optimally contribute to gains in relevance through search personalization. Our key findings include: historic behavior provides substantial benefits at the start of a search session; short-term session behavior contributes the majority of gains in an extended search session; and the combination of session and historic behavior out-performs using either alone. We also characterize how the relative contribution of each model changes throughout the duration of a session. Our findings have implications for the design of search systems that leverage user behavior to personalize the search experience.}}, added-at = {2018-03-19T12:24:51.000+0100}, address = {New York, NY, USA}, author = {Bennett, Paul N. and White, Ryen W. and Chu, Wei and Dumais, Susan T. and Bailey, Peter and Borisyuk, Fedor and Cui, Xiaoyuan}, biburl = {https://www.bibsonomy.org/bibtex/2fa21f1710fe5be8aac6ead355c6efa8a/aho}, booktitle = {Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval}, citeulike-article-id = {11093758}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=2348312}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/2348283.2348312}, doi = {10.1145/2348283.2348312}, interhash = {e4d5726c5e96a9f773f3f9f96b1faad2}, intrahash = {fa21f1710fe5be8aac6ead355c6efa8a}, isbn = {978-1-4503-1472-5}, keywords = {adaptive-search}, location = {Portland, Oregon, USA}, pages = {185--194}, posted-at = {2013-12-09 23:39:08}, priority = {2}, publisher = {ACM}, series = {SIGIR '12}, timestamp = {2018-03-19T12:24:51.000+0100}, title = {{Modeling the Impact of Short- and Long-term Behavior on Search Personalization}}, url = {http://dx.doi.org/10.1145/2348283.2348312}, year = 2012 }