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Evaluation in Contextual Information Retrieval: Foundations and Recent Advances within the Challenges of Context Dynamicity and Data Privacy

Abstract : Context such as the user's search history, demographics, devices, and surroundings, has become prevalent in various domains of information seeking and retrieval such as mobile search, task-based search, and social search. While evaluation is central and has a long history in information retrieval, it faces the big challenge of designing an appropriate methodology that embeds the context into evaluation settings. In this article, we present a unified summary of a wide range of main and recent progress in contextual information retrieval evaluation that leverages diverse context dimensions and uses different principles, methodologies, and levels of measurements. More specifically, this survey article aims to fill two main gaps in the literature: First, it provides a critical summary and comparison of existing contextual information retrieval evaluation method- ologies and metrics according to a simple stratification model; second, it points out the impact of context dynamicity and data privacy on the evaluation design. Finally, we recommend promising research directions for future investigations.
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Lynda Tamine-Lechani, Mariam Daoud. Evaluation in Contextual Information Retrieval: Foundations and Recent Advances within the Challenges of Context Dynamicity and Data Privacy. ACM Computing Surveys, Association for Computing Machinery, 2018, 51 (4), pp.1-36. ⟨10.1145/3204940⟩. ⟨hal-02316830⟩

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