Promoting Positive Post-Click Experience for In-Stream Yahoo Gemini Users

By Mounia Lalmas, Janette Lehmann, Guy Shaked, Fabrizio Silvestri, Gabriele Tolomei. 2015

34 citations recorded by the source corpus.

Click-through rate (CTR) is the most common metric used to assess the performance of an online advert; another performance of an online advert is the user post-click experience. In this paper, we describe the method we have implemented in Yahoo Gemini to measure the post-click experience on Yahoo mobile news streams via an automatic analysis of advert landing pages. We measure the post-click experience by means of two well-known metrics, dwell time and bounce rate. We show that these metrics can be used as proxy of an advert post-click experience, and that a negative post-click experience has a negative effect on user engagement and future ad clicks. We then put forward an approach that analyses advert landing pages, and show how these can affect dwell time and bounce rate. Finally, we develop a prediction model for advert quality based on dwell time, which was deployed on Yahoo mobile news stream app running on iOS. The results show that, using dwell time as a proxy of post-click experience, we can prioritise higher quality ads. We demonstrate the impact of this on users via A/B testing.

Subfields: Computer Vision and Pattern Recognition, Marketing, Sociology and Political Science

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Mounia Lalmas
2. Fabrizio Silvestri
3. Andrew Haines
4. Akira Tajima
5. Ke Zhou
6. Yue Shi
7. Qingyun Wu
8. Miriam Redi

In Marketing

1. Mounia Lalmas
2. Fabrizio Silvestri
3. Yue Shi
4. Qingyun Wu
5. Hongning Wang
6. Liangjie Hong

In Sociology and Political Science

1. Mounia Lalmas
2. Fabrizio Silvestri
3. Ke Zhou
4. Yue Shi
5. Andrew Haines
6. Miriam Redi
7. B. Liu

Citations in the corpus

• Interpretable Predictions of Tree-based Ensembles via Actionable Feature Tweaking (2017, methodology): “We focus on the latter, the post-click experience, following from [1, 12].”
• Improving Post-Click User Engagement on Native Ads via Survival Analysis (2016, methodology): “This set extends features discussed in previous work [21].”
• Embedding-based News Recommendation for Millions of Users (2017, background): “Advertising is also important, but several studies [2, 10] have already reported on the relationship between advertising and user satisfaction, so such discussion has been omitted here.”
• Predicting Pre-click Quality for Native Advertisements (2016, methodology): “Bounce Rate [18] 1 the fraction of sessions with ad landing page dwell time shorter than”
• Returning is Believing: Optimizing Long-term User Engagement in Recommender Systems (2017, background): “[17] measured users’ post-click experience by two metrics: dwell time and bounce rate.”