%0 Conference Paper %1 hu2011characterizing %A Hu, Botao %A Zhang, Yuchen %A Chen, Weizhu %A Wang, Gang %A Yang, Qiang %B Proceedings of the 20th international conference on World wide web %C New York, NY, USA %D 2011 %I ACM %K check diversity implicit-feedback model social-search user-behaviour %P 17--26 %R 10.1145/1963405.1963412 %T Characterizing search intent diversity into click models %U http://doi.acm.org/10.1145/1963405.1963412 %X Modeling a user's click-through behavior in click logs is a challenging task due to the well-known position bias problem. Recent advances in click models have adopted the examination hypothesis which distinguishes document relevance from position bias. In this paper, we revisit the examination hypothesis and observe that user clicks cannot be completely explained by relevance and position bias. Specifically, users with different search intents may submit the same query to the search engine but expect different search results. Thus, there might be a bias between user search intent and the query formulated by the user, which can lead to the diversity in user clicks. This bias has not been considered in previous works such as UBM, DBN and CCM. In this paper, we propose a new <i>intent hypothesis</i> as a complement to the examination hypothesis. This hypothesis is used to characterize the bias between the user search intent and the query in each search session. This hypothesis is very general and can be applied to most of the existing click models to improve their capacities in learning unbiased relevance. Experimental results demonstrate that after adopting the intent hypothesis, click models can better interpret user clicks and achieve a significant NDCG improvement. %@ 978-1-4503-0632-4

@inproceedings{hu2011characterizing, abstract = {Modeling a user's click-through behavior in click logs is a challenging task due to the well-known position bias problem. Recent advances in click models have adopted the examination hypothesis which distinguishes document relevance from position bias. In this paper, we revisit the examination hypothesis and observe that user clicks cannot be completely explained by relevance and position bias. Specifically, users with different search intents may submit the same query to the search engine but expect different search results. Thus, there might be a bias between user search intent and the query formulated by the user, which can lead to the diversity in user clicks. This bias has not been considered in previous works such as UBM, DBN and CCM. In this paper, we propose a new <i>intent hypothesis</i> as a complement to the examination hypothesis. This hypothesis is used to characterize the bias between the user search intent and the query in each search session. This hypothesis is very general and can be applied to most of the existing click models to improve their capacities in learning unbiased relevance. Experimental results demonstrate that after adopting the intent hypothesis, click models can better interpret user clicks and achieve a significant NDCG improvement.}, acmid = {1963412}, added-at = {2011-07-29T16:30:35.000+0200}, address = {New York, NY, USA}, author = {Hu, Botao and Zhang, Yuchen and Chen, Weizhu and Wang, Gang and Yang, Qiang}, biburl = {https://www.bibsonomy.org/bibtex/2e100ef41d01dd0bed3b9b3daa33aaef3/beate}, booktitle = {Proceedings of the 20th international conference on World wide web}, description = {Characterizing search intent diversity into click models}, doi = {10.1145/1963405.1963412}, interhash = {142e96b93b4a11eaa23ba8f73f419eed}, intrahash = {e100ef41d01dd0bed3b9b3daa33aaef3}, isbn = {978-1-4503-0632-4}, keywords = {check diversity implicit-feedback model social-search user-behaviour}, location = {Hyderabad, India}, numpages = {10}, pages = {17--26}, publisher = {ACM}, series = {WWW '11}, timestamp = {2011-07-29T16:30:35.000+0200}, title = {Characterizing search intent diversity into click models}, url = {http://doi.acm.org/10.1145/1963405.1963412}, year = 2011 }