A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
A study of smoothing methods for language models applied to information retrieval
ACM Transactions on Information Systems (TOIS)
Selecting good expansion terms for pseudo-relevance feedback
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Optimal rare query suggestion with implicit user feedback
Proceedings of the 19th international conference on World wide web
Efficient and effective spam filtering and re-ranking for large web datasets
Information Retrieval
Probabilistic models for personalizing web search
Proceedings of the fifth ACM international conference on Web search and data mining
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Session search is the Information Retrieval (IR) task that performs document retrieval for an entire session. During a session, users often change queries to explore and investigate the information needs. In this paper, we propose to use query change as a new form of relevance feedback for better session search. Evaluation conducted over TREC 2012 Session Track shows that query change is a highly effective form of feedback as compared with existing relevance feedback methods. The proposed method outperforms the state-of-the-art relevance feedback methods for the TREC 2012 Session Track by a significant improvement of 25%.