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Understanding an Enriched Multidimensional User Relevance Model by Analyzing Query Logs

Li, Jingfei; Zhang, Peng; Song, Dawei and Wu, Yue (2017). Understanding an Enriched Multidimensional User Relevance Model by Analyzing Query Logs. Journal of the Association for Information Science and Technology, 68(12) pp. 2743–2754.

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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 paper, 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, i.e., 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.

Item Type: Journal Item
Copyright Holders: 2017 ASIS&T
ISSN: 2330-1643
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Item ID: 48556
Depositing User: Dawei Song
Date Deposited: 13 Feb 2017 16:42
Last Modified: 29 May 2020 09:48
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