Proceedings of the 11th international conference on World Wide Web
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Scaling personalized web search
WWW '03 Proceedings of the 12th 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
Context-sensitive information retrieval using implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Query chains: learning to rank from implicit feedback
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Adapting ranking SVM to document retrieval
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Mining long-term search history to improve search accuracy
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Summarizing local context to personalize global web search
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Support Vector Ordinal Regression
Neural Computation
Ranking with multiple hyperplanes
SIGIR '07 Proceedings of the 30th 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
To personalize or not to personalize: modeling queries with variation in user intent
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Evaluating the Effectiveness of Personalized Web Search
IEEE Transactions on Knowledge and Data Engineering
LETOR: A benchmark collection for research on learning to rank for information retrieval
Information Retrieval
Improving re-ranking of search results using collaborative filtering
AIRS'06 Proceedings of the Third Asia conference on Information Retrieval Technology
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In this paper we present a framework for improving the ranking learning process, taking into account the implicit search behaviors of users. Our approach is query-centric. That is, it examines the search behaviors induced by queries and groups together queries with similar such behaviors, forming search behavior clusters. Then, it trains multiple ranking functions, each one corresponding to one of these clusters. The trained models are finally combined to re-rank the results of each new query, taking into account the similarity of the query with each cluster. The main idea is that similar search behaviors can be detected and exploited for result re-ranking by analysing results into feature vectors, and clustering them. The experimental evaluation shows that our method improves the ranking quality of a state of the art ranking model.