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Creators/Authors contains: "Montazeralghaem, Ali"

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  1. ; ; (, Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR2020))
    Relevance feedback is an effective technique for improving retrieval performance using the feedback documents. Selecting effective feedback terms and weighting them have been always challenging. Several methods based on different assumptions have been so far proposed, however, they do not directly optimize the retrieval performance. Learning an effective relevance feedback model is not trivial since the true feedback distribution is unknown. In this paper, we propose a general reinforcement learning framework for relevance feedback, called RML. Our framework directly optimizes any desired retrieval metric, including precision-oriented, recall-oriented, and even diversity metrics. RML can be easily extended to directly optimize any arbitrary user satisfaction signal. Experiments on standard TREC collections demonstrate the effectiveness of our framework. 
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  2. ; ; (, Proceedings of International Conference on the Theory of Information Retrieval Conference (ICTIR 2019))
    Term discrimination value is among the three basic heuristics exploited, directly or indirectly, in almost all ranking models for ad-hoc Information Retrieval (IR). Query term discrimination in monolingual IR is usually estimated based on document or collection frequency of terms. In query translation approach for CLIR, discrimination value of a query term needs to be estimated based on document or collection frequencies of its translations, which is more challenging. We show that the existing estimation models do not correctly estimate and adequately reflect the difference between discrimination power of query terms, which hurts the retrieval performance. We then propose a new model to estimate discrimination values of query terms for CLIR and empirically demonstrate its impact in improving the CLIR performance. 
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