We are pleased to announce that researchers from our Personalization Sciences team at Yahoo Labs received the Best Paper Award at the 8th ACM Recommender Systems Conference (RecSys 2014) on Wednesday. In their paper "Beyond Clicks: Dwell Time for Personalization," scientist co-authors Xing Yi, Liangjie Hong, Erheng Zhong, Nathan Liu, and Suju Rajan, describe how to utilize dwell time as a better user engagement metric than click-through rate (CTR) in content recommendation systems. Many internet companies, such as Yahoo, Facebook, Google and Twitter, rely on content recommendation systems to deliver the most relevant content items to individual users through personalization. Delivering such personalized user experiences is believed to increase the long term engagement of users. While there has been a lot of progress in designing effective personalized recommender systems, by exploiting user interests and historical interaction data through implicit (item click) or explicit (item rating) feedback, directly optimizing for users’ satisfaction with the system remains challenging. In the paper, the authors explore the idea of using item-level dwell time as a proxy to quantify how likely a content item is relevant to a particular user. The paper describes a novel method to compute accurate dwell time based on client-side and server-side logging and demonstrates how to normalize dwell time across different devices and contexts. In addition, the authors describe their experiments in incorporating dwell time into state-of-the-art learning to rank techniques and collaborative filtering models that obtain competitive performances in both offline and online settings. Author Xing Yi presents "Beyond Clicks: Dwell Time for Personalization" at RecSys in the first 20 minutes of this video: http://youtu.be/1jHxGCl8RXc?list=UU2nEn-yNA1BtdDNWziphPGA The ACM Recommender System conference (RecSys) is the premier international forum for the presentation of new research results, systems and techniques in the broad field of recommender systems. Recommendation is a particular form of information filtering, that exploits past behaviors and user similarities to generate a list of information items that is personally tailored to an end-user’s preferences. As RecSys brings together the main international research groups working on recommender systems, along with many of the world’s leading e-commerce companies, it has become the most important annual conference for the presentation and discussion of recommender system research.