TY - GEN
T1 - Beyond ten blue links
T2 - 5th ACM International Conference on Web Search and Data Mining, WSDM 2012
AU - Chen, Danqi
AU - Chen, Weizhu
AU - Wang, Haixun
AU - Chen, Zheng
AU - Yang, Qiang
N1 - Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2012
Y1 - 2012
N2 - Click models have been positioned as an effective approach to interpret user click behavior in search engines. Existing click models mostly focus on traditional Web search that considers only ten homogeneous Web HTML documents that appear on the first search-result page. However, in modern commercial search engines, more and more Web search results are federated from multiple sources and contain non-HTML results returned by other heterogeneous vertical engines, such as video or image search engines. In this paper, we study user click behavior in federated search. We observed that user click behavior in federated search is highly different from that in traditional Web search, making it difficult to interpret using existing click models. In response, we propose a novel federated click model (FCM) to interpret user click behavior in federated search. In particular, we take into considerations two new biases in FCM. The first comes from the observation that users tend to be attracted by vertical results and their visual attention on them may increase the examination probability of other nearby web results. The other illustrates that user click behavior on vertical results may lead to more clues of search relevance due to their presentation style in federated search. With these biases and an effective model to correct them, FCM is more accurate in characterizing user click behavior in federated search. Our extensive experimental results show that FCM can outperform other click models in interpreting user click behavior in federated search and achieve significant improvements in terms of both perplexity and log-likelihood.
AB - Click models have been positioned as an effective approach to interpret user click behavior in search engines. Existing click models mostly focus on traditional Web search that considers only ten homogeneous Web HTML documents that appear on the first search-result page. However, in modern commercial search engines, more and more Web search results are federated from multiple sources and contain non-HTML results returned by other heterogeneous vertical engines, such as video or image search engines. In this paper, we study user click behavior in federated search. We observed that user click behavior in federated search is highly different from that in traditional Web search, making it difficult to interpret using existing click models. In response, we propose a novel federated click model (FCM) to interpret user click behavior in federated search. In particular, we take into considerations two new biases in FCM. The first comes from the observation that users tend to be attracted by vertical results and their visual attention on them may increase the examination probability of other nearby web results. The other illustrates that user click behavior on vertical results may lead to more clues of search relevance due to their presentation style in federated search. With these biases and an effective model to correct them, FCM is more accurate in characterizing user click behavior in federated search. Our extensive experimental results show that FCM can outperform other click models in interpreting user click behavior in federated search and achieve significant improvements in terms of both perplexity and log-likelihood.
KW - Click model
KW - Federated search
KW - Log analysis
UR - http://www.scopus.com/inward/record.url?scp=84863253278&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84863253278&partnerID=8YFLogxK
U2 - 10.1145/2124295.2124351
DO - 10.1145/2124295.2124351
M3 - Conference contribution
AN - SCOPUS:84863253278
SN - 9781450307475
T3 - WSDM 2012 - Proceedings of the 5th ACM International Conference on Web Search and Data Mining
SP - 463
EP - 472
BT - WSDM 2012 - Proceedings of the 5th ACM International Conference on Web Search and Data Mining
PB - Association for Computing Machinery
Y2 - 8 February 2012 through 12 February 2012
ER -