A user browsing model to predict search engine click data from past observations.
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
A dynamic bayesian network click model for web search ranking
Proceedings of the 18th international conference on World wide web
Characterizing search intent diversity into click models
Proceedings of the 20th international conference on World wide web
Smoothing click counts for aggregated vertical search
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Improving searcher models using mouse cursor activity
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
WSCD2013: workshop on web search click data 2013
Proceedings of the sixth ACM international conference on Web search and data mining
Using intent information to model user behavior in diversified search
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
Click model-based information retrieval metrics
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
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Most modern web search engines yield a list of documents of a fixed length (usually 10) in response to a user query. The next ten search results are usually available in one click. These documents either replace the current result page or are appended to the end. Hence, in order to examine more documents than the first 10 the user needs to explicitly express her intention. Although clickthrough numbers are lower for documents on the second and later result pages, they still represent a noticeable amount of traffic. We propose a modification of the Dynamic Bayesian Network (DBN) click model by explicitly including into the model the probability of transition between result pages. We show that our new click model can significantly better capture user behavior on the second and later result pages while giving the same performance on the first result page.