Modeling Multidimensional User Relevance in IR using Vector Spaces

Uprety, Sagar; Su, Yi; Song, Dawei and Li, Jingfei (2018). Modeling Multidimensional User Relevance in IR using Vector Spaces. In: SIGIR '18 The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, 8-12 Jul 2018, Ann Arbor, MI, USA, ACM Press, pp. 993–996.



It has been shown that relevance judgment of documents is influenced by multiple factors beyond topicality. Some multidimensional user relevance models (MURM) proposed in literature have investigated the impact of different dimensions of relevance on user judgment. Our hypothesis is that a user might give more importance to certain relevance dimensions in a session which might change dynamically as the session progresses. This motivates the need to capture the weights of different relevance dimensions using feedback and build a model to rank documents for subsequent queries according to these weights. We propose a geometric model inspired by the mathematical framework of Quantum theory to capture the user's importance given to each dimension of relevance and test our hypothesis on data from a web search engine and TREC Session track.

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