Effects of User Negative Experience in Mobile News Streaming

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval(2019)

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摘要
Online news streaming services have been one of the major information acquisition resources for mobile users. In many cases, users click an article but find it cannot satisfy or even annoy them. Intuitively, these negative experiences will affect users' behaviors and satisfaction, but such effects have not been well understood. In this work, a retrospective analysis is conducted using real users' log data, containing user's explicit feedback of negative experiences, from a commercial news streaming application. Through multiple intra-session comparison experiments, we find that in current session, users will spend less time reading the content, lose activeness and leave sooner after having negative experiences. Later return and significant changes of user behaviors in the next session are also observed, which demonstrates the existence of inter-session effects of negative experiences. Since users' negative experiences are generally implicit, we further investigate the possibility and the approach to automatically identify them. Results show that using changes of both users' intra-session and inter-session behaviors achieves significant improvement. Besides the effects on user behaviors, we also explore the effects on user satisfaction by incorporating a laboratory user study. Results show that negative experiences reduce user satisfaction in the current session, and the impact will last to the next session. Moreover, we demonstrate users' negative feedback helps on the meta-evaluation of online metrics. Our research has comprehensively analyzed the impacts of users' item-level negative experiences, and shed light on the understanding of user behaviors and satisfaction.
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关键词
log analysis, negative experience, news recommendation, user behavior modeling, user satisfaction
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