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Mining Large-scale TV Group Viewing Patterns for Group Recommendation

Allison Chaney, Mike Gartrell, Jake Hofman, John Guiver, Noam Koenigstein, Pushmeet Kohli, and Ulrich Paquet

Abstract

We present a large-scale study of television viewing habits, focusing on how individuals adapt their preferences when consuming content in group settings. While there has been a great deal of recent work on modeling individual preferences, there has been considerably less work studying the behavior and preferences of groups, due mostly to the difficulty of data collection in these settings. In contrast to past work that has relied either on small-scale surveys or prototypes, we explore more than 4 million logged views paired with individual-level demographic and co-viewing information to uncover variation in the viewing patterns of individuals and groups. Our analysis reveals which genres are popular among specific demographic groups when viewed individually, how often individuals from different demographic categories participate in group viewing, and how viewing patterns change in various group contexts. Furthermore, we leverage this large-scale dataset to directly estimate how individual preferences are combined in group settings, fifinding subtle deviations from traditional preference aggregation functions. We present a simple model which captures these effects and discuss the impact of these findings on the design of group recommendation systems.

Details

Publication typeTechReport
NumberMSR-TR-2013-114
PublisherMicrosoft Technical Report
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