Abstract
Traditional information retrieval systems are evaluated with the assumption that each query is independent. However, during their interactions with search engines, many users find themselves reformulating their queries in order to satisfy their information need. In this paper, we investigate the impact of using ranking aggregation in the context of session information retrieval. First, we identify useful sources for terms to be used as related queries. For each query, we generate related queries from various sources and use those multiple representations of a query to obtain several rankings that we combine using simple rank aggregation methods. We compare the effects of using each source and show that some sources can provide up to 46% increase in nDCG@10 over our dirichlet-smoothed language model baseline and our best result is competitive with all TREC Session track systems for 2011 and 2012.
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Bah, A., Carterette, B. (2014). Aggregating Results from Multiple Related Queries to Improve Web Search over Sessions. In: Jaafar, A., et al. Information Retrieval Technology. AIRS 2014. Lecture Notes in Computer Science, vol 8870. Springer, Cham. https://doi.org/10.1007/978-3-319-12844-3_15
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DOI: https://doi.org/10.1007/978-3-319-12844-3_15
Publisher Name: Springer, Cham
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