Description:
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, finding
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.
Source: Microsoft Research
Download pdf report: Mining Large-scale TV Group Viewing Patterns for Group Recommendation
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