Quality Effects on User Preferences and Behaviorsin Mobile News Streaming

User behaviors are widely used as implicit feedbacks of user preferences in personalized information systems. In previous works and online applications, the user's click signals are used as positive feedback for ranking, recommendation, evaluation, etc. However, when users click on a piece of low-quality news, they are more likely to have negative experiences and different reading behaviors. Hence, the ignorance of the quality effects of news may lead to the misinterpretation of user behaviors as well as consequence studies. To address these issues, we conducted an in-depth user study in mobile news streaming scenario to investigate whether and how the quality of news may affect user preferences and user behaviors. Firstly, we verify that quality does affect user preferences, and low-quality news results in a lower preference. We further find that this effect varies with both interaction phases and user's interest in the topic of the news. Secondly, we inspect how users interact with low-quality news. Surprisingly, we find that users are more likely to click on low-quality news because of its high title persuasion. Moreover, users will read less and slower with fewer revisits and examinations while reading the low-quality news.

Based on these quality effects we have discovered, we propose the Preference Behavior Quality (PBQ) probability model which incorporates the quality into traditional behavior-only implicit feedback. The significant improvement demonstrates that incorporating quality can help build implicit feedback. Since the importance and difficulty in collecting news quality, we further investigate how to identify it automatically. Based on point-wise and pair-wise distinguishing experiments, we show that user behaviors, especially reading ratio and dwell time, have high ability to identify news quality. Our research has comprehensively analyzed the effects of quality on user preferences and behaviors, and raised the awareness of item quality in interpreting user behaviors and estimating user preferences.