Analysis of User Dwell Time on Non-News Pages
| Autor: | Mitsuo Yoshida, Kyoji Umemura, Keiichi Soejima, Ryosuke Homma |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Focus (computing) Information retrieval Computer science InformationSystems_INFORMATIONINTERFACESANDPRESENTATION(e.g. HCI) Computer Science - Human-Computer Interaction 02 engineering and technology Human-Computer Interaction (cs.HC) Dwell time InformationSystems_MODELSANDPRINCIPLES Index (publishing) 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing |
| Zdroj: | IEEE BigData |
| DOI: | 10.48550/arxiv.1903.00213 |
| Popis: | There is dwell time as one of the indicators of user's behavior, and this indicates how long a user looked at a page. Dwell time is especially useful in fields where user ratings are important, such as search engines, recommender systems, and advertisements are important. Despite the importance of this index, however, its characteristics are not well known. In this paper, we analyze the dwell times of various websites by desktop and mobile devices using data of one year. Our aim is to clarify the characteristics of dwell time on non-news websites in order to discover which features are effective for predicting the dwell time. In this analysis, we focus on device types, access times, behavior on the website, and scroll depth. The results indicated that the number of sessions decreased as the dwell time increased, for both desktop and mobile devices. We also found that hour and month greatly affected the dwell time, but day of the week had little effect. Moreover, we discovered that inside and click users tended to have longer dwell times than outside and non-click users. However, we can not find a relationship between dwell time and scroll depth. This is because even if a user browsed the bottom of the page, the user might not necessarily have read the entire page. Comment: IEEE BigData 2018 Workshop : The 3rd International Workshop on Application of Big Data for Computational Social Science (ABCSS2018). 2018 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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