Efficient Context Management and Personalized User Recommendations in a Smart Social TV environment

TL;DR

This paper introduces a novel context management model and personalized recommendation system for Smart Social TV environments, leveraging user-item graph analysis and collaborative filtering to enhance user experience.

Contribution

It proposes an innovative context management framework and recommendation service tailored for Smart TV and second screen environments, integrating social media and online data sources.

Findings

The model improves recommendation accuracy and relevance.

Evaluation shows enhanced efficiency and effectiveness over existing methods.

Added value demonstrated through dataset analysis.

Abstract

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.