The paper details how modeling people's change in tastes over time affects their ratings. From the CACM article:
...although recent data may reveal more about a user's current preferences than older data, simply underweighting older ratings loses too much valuable information for that approach to work. The trick to not tossing the baby with the bathwater is to retain everything that predicts the user's long-term behavior while filtering out temporary noise.Fascinating. I look forward to reading this paper in detail. This has broad implications for leveraging user data in social media applications.