Towards interactive query expansion
SIGIR '88 Proceedings of the 11th annual international ACM SIGIR conference on Research and development in information retrieval
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 6th international conference on Intelligent user interfaces
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Understanding user goals in web search
Proceedings of the 13th international conference on World Wide Web
Adaptive web search based on user profile constructed without any effort from users
Proceedings of the 13th international conference on World Wide Web
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
Evaluating implicit measures to improve web search
ACM Transactions on Information Systems (TOIS)
Accurately interpreting clickthrough data as implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Evaluating implicit feedback models using searcher simulations
ACM Transactions on Information Systems (TOIS)
Automatic identification of user interest for personalized search
Proceedings of the 15th international conference on World Wide Web
Learning user interaction models for predicting web search result preferences
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
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
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Implicit relevance feedback has proved to be a important resource in improving search accuracy and personalization. However, researchers who rely on feedback data for testing their algorithms or other personalization related problems are loomed with problems like unavailability of data, staling up of data and so on. Given these problems, we are motivated towards creating a synthetic user relevance feedback data, based on insights from query log analysis. We call this simulated feedback. We believe that simulated feedback can be immensely beneficial to web search engine and personalization research communities by greatly reducing efforts involved in collecting user feedback. The benefits from "Simulated feedback" are - it is easy to obtain and also the process of obtaining the feedback data is repeatable, customizable and does not need the interactions of the user. In this paper, we describe a simple yet effective approach for creating simulated feedback. We have evaluated our system using the clickthrough data of the users and achieved 77% accuracy in generating click-through data.