Papers › Dynamic Facet Selection by Maximizing Graded Relevance
Dynamic Facet Selection by Maximizing Graded Relevance
Michael Glass, Md Faisal Mahbub Chowdhury, Yu Deng, Ruchi Mahindru, Nicolas Rodolfo Fauceglia, Alfio Gliozzo, Nandana Mihindukulasooriya
Dynamic faceted search (DFS), an interactive query refinement technique, is a form of Human–computer information retrieval (HCIR) approach. It allows users to narrow down search results through facets, where the facets-documents mapping is determined at runtime based on the context of user query instead of pre-indexing the facets statically. In this paper, we propose a new unsupervised approach for dynamic facet generation, namely optimistic facets, which attempts to generate the best possible subset of facets, hence maximizing expected Discounted Cumulative Gain (DCG), a measure of ranking quality that uses a graded relevance scale. We also release code to generate a new evaluation dataset. Through empirical results on two datasets, we show that the proposed DFS approach considerably improves the document ranking in the search results.
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