Understanding Reading Attention Distribution during Relevance Judgement
| Autor: | Min Zhang, Xiangsheng Li, Jiaxin Mao, Shaoping Ma, Zexue He, Yiqun Liu |
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| Rok vydání: | 2018 |
| Předmět: |
Matching (statistics)
Information retrieval Computer science media_common.quotation_subject 05 social sciences Judgement 02 engineering and technology 050105 experimental psychology Ranking (information retrieval) Task (project management) Ranking 020204 information systems Reading (process) 0202 electrical engineering electronic engineering information engineering Question answering 0501 psychology and cognitive sciences Relevance (information retrieval) media_common |
| Zdroj: | CIKM |
| DOI: | 10.1145/3269206.3271764 |
| Popis: | Reading is a complex cognitive activity in many information retrieval related scenarios, such as relevance judgement and question answering. There exists plenty of works which model these processes as a matching problem, which focuses on how to estimate the relevance score between a document and a query. However, little is known about what happened during the reading process, i.e., how users allocate their attention while reading a document during a specific information retrieval task. We believe that a better understanding of this process can help us design better weighting functions inside the document and contributes to the improvement of ranking performance. In this paper, we focus on the reading process during relevance judgement task. We designed a lab-based user study to investigate human reading patterns in assessing a document, where users' eye movements and their labeled relevant text were collected, respectively. Through a systematic analysis into the collected data, we propose a two-stage reading model which consists of a preliminary relevance judgement stage (Stage 1) and a reading with preliminary relevance stage (Stage 2). In addition, we investigate how different behavior biases affect users' reading behaviors in these two stages. Taking these biases into consideration, we further build prediction models for user's reading attention. Experiment results show that query independent features outperform query dependent features, which indicates that users allocate attentions based on many signals other than query terms in this process. Our study sheds light on the understanding of users' attention allocation during relevance judgement and provides implications for improving the design of existing ranking models. |
| Databáze: | OpenAIRE |
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