Accurately interpreting clickthrough data as implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
A regression framework for learning ranking functions using relative relevance judgments
SIGIR '07 Proceedings of the 30th 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
Global ranking by exploiting user clicks
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
A model to estimate intrinsic document relevance from the clickthrough logs of a web search engine
Proceedings of the third ACM international conference on Web search and data mining
Proceedings of the fourth ACM international conference on Web search and data mining
Proceedings of the 20th international conference on World wide web
A unified search federation system based on online user feedback
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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We explore the potential of using users click-through logs where no editorial judgment is available to improve the ranking function of a vertical search engine. We base our analysis on the Cumulate Relevance Model, a user behavior model recently proposed as a way to extract relevance signal from click-through logs. We propose a novel way of directly learning the ranking function, effectively by-passing the need to have explicit editorial relevance label for each query-document pair. This approach potentially adjusts more closely the ranking function to a variety of user behaviors both at the individual and at the aggregate levels. We investigate two ways of using behavioral model; First, we consider the parametric approach where we learn the estimates of document relevance and use them as targets for the machine learned ranking schemes. In the second, functional approach, we learn a function that maximizes the behavioral model likelihood, effectively by-passing the need to estimate a substitute for document labels. Experiments using user session data collected from a commercial vertical search engine demonstrate the potential of our approach. While in terms of DCG, the editorial model out-perform the behavioral one, online experiments show that the behavioral model is on par --if not superior-- to the editorial model. To our knowledge, this is the first report in the Literature of a competitive behavioral model in a commercial setting