Investigating the Effects of Popularity Data on Predictive Relevance Judgments in Academic Search Systems
Behnert, C. (2019). Investigating the Effects of Popularity Data on Predictive Relevance Judgments in Academic Search Systems. In Proceedings of the 2019 Conference on Human Information Interaction and Retrieval – CHIIR ’19 (S. 437–440). New York, New York, USA: ACM Press.
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The elements of a surrogate serve as clues to relevance. They may be seen as operationalized relevance criteria by which users judge the relevance of a search result according to their information need. In addition to short textual summaries, today’s academic search systems integrate additional data into their search results presentation, for example, the number of citations or the number of downloads. This kind of data can be described as popularity data, serving as factors also incorporated in search engines’ ranking algorithms. Past research shows that there are diverse criteria and factors involved in relevance judgements from the user perspective. However, previous empirical studies on relevance criteria and clues examined surrogates that did not include popularity data. The goal of my doctoral research is to gain significant knowledge on the criteria by which users in an academic search situation make relevance judgements based on surrogates that include popularity data. This paper describes the current state of the experimental research design and method of data collection.