Efficient Context Management and Personalized User Recommendations in a Smart Social TV environment
This addresses the problem of delivering sophisticated recommendations in Smart TV systems for end-users, though it appears incremental in combining existing techniques.
The paper tackles the challenge of user context management and personalized recommendations in Smart Social TV environments by presenting a Context Management model and recommendation service using user-item graph analysis and collaborative filtering. The evaluation on online datasets shows improved efficiency and effectiveness compared to current approaches.
With the emergence of Smart TV and related interconnected devices, second screen solutions have rapidly appeared to provide more content for end-users and enrich their TV experience. Given the various data and sources involved - videos, actors, social media and online databases- the aforementioned market poses great challenges concerning user context management and sophisticated recommendations that can be addressed to the end-users. This paper presents an innovative Context Management model and a related first and second screen recommendation service, based on a user-item graph analysis as well as collaborative filtering techniques in the context of a Dynamic Social & Media Content Syndication (SAM) platform. The model evaluation provided is based on datasets collected online, presenting a comparative analysis concerning efficiency and effectiveness of the current approach, and illustrating its added value.