On term selection for query expansion
Journal of Documentation
Improving the effectiveness of information retrieval with local context analysis
ACM Transactions on Information Systems (TOIS)
An information-theoretic approach to automatic query expansion
ACM Transactions on Information Systems (TOIS)
Relevance based language models
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Model-based feedback in the language modeling approach to information retrieval
Proceedings of the tenth international conference on Information and knowledge management
A study of smoothing methods for language models applied to information retrieval
ACM Transactions on Information Systems (TOIS)
A Markov random field model for term dependencies
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Exploring social annotations for the semantic web
Proceedings of the 15th international conference on World Wide Web
Improving the estimation of relevance models using large external corpora
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Optimizing web search using social annotations
Proceedings of the 16th international conference on World Wide Web
Using query contexts in information retrieval
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Selecting good expansion terms for pseudo-relevance feedback
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Real-time automatic tag recommendation
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Building enriched document representations using aggregated anchor text
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Multinomial randomness models for retrieval with document fields
ECIR'07 Proceedings of the 29th European conference on IR research
Exploring social annotation tags to enhance information retrieval performance
AMT'10 Proceedings of the 6th international conference on Active media technology
Information retrieval in folksonomies: search and ranking
ESWC'06 Proceedings of the 3rd European conference on The Semantic Web: research and applications
Proximity-based rocchio's model for pseudo relevance
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
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Pseudo relevance feedback (PRF) via query expansion assumes that the top ranked documents from the first-pass retrieval are relevant. The most informative terms in the pseudo relevant documents are then used to update the original query representation in order to boost the retrieval performance. Most current PRF approaches estimate the importance of the candidate expansion terms based on their statistics on document level. However, in traditional PRF approaches, the context information is always ignored in traditional query expansion models. Therefore, off-topic terms can also be selected, which may result in a decrease of retrieval performance. In this paper, we propose a context-based feedback framework based on Bayesian network, in which multiple context information can be taken into account. In order to demonstrate the effectiveness of our framework, we explore two different kinds of context in our experiments. The experimental results show that our proposed algorithm performs significantly better than a strong PRF baseline.