Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
The concept of relevance in IR
Journal of the American Society for Information Science and Technology
Eye-tracking analysis of user behavior in WWW search
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Improving web search ranking by incorporating user behavior information
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Predicting clicks: estimating the click-through rate for new ads
Proceedings of the 16th international conference on World Wide Web
An experimental comparison of click position-bias models
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Efficient multiple-click models in web search
Proceedings of the Second ACM International Conference on Web Search and Data Mining
A dynamic bayesian network click model for web search ranking
Proceedings of the 18th international conference on World wide web
Click chain model in web search
Proceedings of the 18th international conference on World wide web
BBM: bayesian browsing model from petabyte-scale data
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Including summaries in system evaluation
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Modeling contextual factors of click rates
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
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
A novel click model and its applications to online advertising
Proceedings of the third ACM international conference on Web search and data mining
Competing for users' attention: on the interplay between organic and sponsored search results
Proceedings of the 19th international conference on World wide web
Temporal click model for sponsored search
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Incorporating post-click behaviors into a click model
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Context-aware ranking in web search
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
User browsing models: relevance versus examination
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning click models via probit bayesian inference
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Predicting short-term interests using activity-based search context
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Characterizing search intent diversity into click models
Proceedings of the 20th international conference on World wide web
Context-sensitive query auto-completion
Proceedings of the 20th international conference on World wide web
Collaborative competitive filtering: learning recommender using context of user choice
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
User-click modeling for understanding and predicting search-behavior
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
Incorporating vertical results into search click models
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 22nd international conference on World Wide Web
Hi-index | 0.00 |
Recent advances in click model have established it as an attractive approach to infer document relevance. Most of these advances consider the user click/skip behavior as binary events but neglect the context in which a click happens. We show that real click behavior in industrial search engines is often noisy and not always a good indication of relevance. For a considerable percentage of clicks, users select what turn out to be irrelevant documents and these clicks should not be directly used as evidence for relevance inference. Thus in this paper, we put forward an observation that the relevance indication degree of a click is not a constant, but can be differentiated by user preferences and the context in which the user makes her click decision. In particular, to interpret the click behavior discriminatingly, we propose a Noise-aware Click Model (NCM) by characterizing the noise degree of a click, which indicates the quality of the click for inferring relevance. Specifically, the lower the click noise is, the more important the click is in its role for relevance inference. To verify the necessity of explicitly accounting for the uninformative noise in a user click, we conducted experiments on a billion-scale dataset. Extensive experimental results demonstrate that as compared with two state-of-the-art click models in Web Search, NCM can better interpret user click behavior and achieve significant improvements in terms of both perplexity and NDCG.