Real life, real users, and real needs: a study and analysis of user queries on the web
Information Processing and Management: an International Journal
Applying summarization techniques for term selection in relevance feedback
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Incorporating user search behavior into relevance feedback
Journal of the American Society for Information Science and Technology
A survey on the use of relevance feedback for information access systems
The Knowledge Engineering Review
The influence of relevance levels on the effectiveness of interactive information retrieval
Journal of the American Society for Information Science and Technology
Query dependent ranking using K-nearest neighbor
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Introduction to Information Retrieval
Introduction to Information Retrieval
Identifying clusters of user behavior in intranet search engine log files
Journal of the American Society for Information Science and Technology
Proceedings of the 18th ACM conference on Information and knowledge management
Using text classification method in relevance feedback
ACIIDS'10 Proceedings of the Second international conference on Intelligent information and database systems: Part II
Simulating simple and fallible relevance feedback
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Learning to Rank for Information Retrieval and Natural Language Processing
Learning to Rank for Information Retrieval and Natural Language Processing
ECIR'06 Proceedings of the 28th European conference on Advances in Information Retrieval
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Relevance feedback (RF) has been studied under laboratory conditions using test collections and either test persons or simple simulation. These studies have given mixed results. Automatic (or pseudo) RF and intellectual RF, both leading to query reformulation, are the main approaches to explicit RF. In the present study we perform RF with the help of classification of search results. We conduct our experiments in a comprehensive collection, namely various TREC ad-hoc collections with 250 topics. We also studied various term space reduction techniques for the classification process. The research questions are: given RF on top results of pseudo RF (PRF) query results, is it possible to learn effective classifiers for the following results? What is the effectiveness of various classification methods? Our findings indicate that this approach of applying RF is significantly more effective than PRF with short (title) queries and long (title and description) queries.