  There have been significant innovations in media technologies in the recent
years. While these developments have improved experiences for individual users,
design of multi-user interfaces still remains a challenge. A relatively
unexplored area in this context, is enabling multiple users to enjoy shared
viewing (e.g. deciding on movies to watch together). In particular, the
challenge is to design an intelligent system which would enable viewers to
explore together shows or movies they like, seamlessly. This is a complex
design problem, as it requires the system to (i) assess affinities of
individual users (movies or genres), (ii) combine individual preferences taking
into account user-user interactions, and (iii) be non-intrusive simultaneously.
The proposed system VoCoG, is an end-to-end intelligent system for
collaborative viewing. VoCoG incorporates an online recommendation algorithm,
efficient methods for analyzing natural conversation and a graph-based method
to fuse preferences of multiple users. It takes user conversation as input,
making it non-intrusive. A usability survey of the system indicates that the
system provides a good experience to the users as well as relevant
recommendations. Further analysis of the usage data reveals insights about the
nature of conversation during the interaction sessions, final consensus among
the users as well as ratings of varied user groups.
