Document (#40962)

Author

Li, J.
Zhang, P.
Song, D.
Wu, Y.

Title

Understanding an enriched multidimensional user relevance model by analyzing query logs

Source

Journal of the Association for Information Science and Technology. 68(2017) no.12, S.2743-2754

Year

2017

Abstract

Modeling multidimensional relevance in information retrieval (IR) has attracted much attention in recent years. However, most existing studies are conducted through relatively small-scale user studies, which may not reflect a real-world and natural search scenario. In this article, we propose to study the multidimensional user relevance model (MURM) on large scale query logs, which record users' various search behaviors (e.g., query reformulations, clicks and dwelling time, etc.) in natural search settings. We advance an existing MURM model (including five dimensions: topicality, novelty, reliability, understandability, and scope) by providing two additional dimensions, that is, interest and habit. The two new dimensions represent personalized relevance judgment on retrieved documents. Further, for each dimension in the enriched MURM model, a set of computable features are formulated. By conducting extensive document ranking experiments on Bing's query logs and TREC session Track data, we systematically investigated the impact of each dimension on retrieval performance and gained a series of insightful findings which may bring benefits for the design of future IR systems.

Content

Vgl.: http://onlinelibrary.wiley.com/doi/10.1002/asi.23868/full.

Theme

Retrievalstudien

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5. Niemi, T.; Hirvonen, L.; Järvelin, K.: Multidimensional data model and query language for informetrics (2003) 0.21

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