LDA-based document models for ad-hoc retrieval
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
A bayesian logistic regression model for active relevance feedback
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
A cluster-based resampling method for pseudo-relevance feedback
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Latent topic feedback for information retrieval
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
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Most of the relevance feedback algorithms only use document terms as feedback (local features) in order to update the query and re-rank the documents to show to the user. This approach is limited by the terms of those documents without any global context. We propose to use statistical topic modeling techniques in relevance feedback to incorporate a better estimate of context by including global information about the document. This is particularly helpful for difficult queries where learning the context from the interactions with the user is crucial. We propose to use the topic mixture information obtained to characterize the documents and learn their topics. Then, we rank documents incorporating positive and negative feedback by fitting a latent distribution for each class of documents online and combining all the features using Bayesian Logistic Regression. We show results using the OHSUMED dataset for 3 different variants and obtain higher performance, up to 12.5% in Mean Average Precision (MAP).