Second Screen User Profiling and Multi-level Smart Recommendations in the context of Social TVs

TL;DR

This paper introduces a context management system for Social TV that leverages social media data to deliver personalized, multi-level recommendations for both first and second screen users, enhancing user engagement.

Contribution

It presents a novel context management mechanism within the SAM project that captures social patterns and applies collaborative filtering for smart content recommendations in Social TV environments.

Findings

Effective personalization of recommendations demonstrated

Improved user engagement through social pattern analysis

Validated approach using real-world movie rating data

Abstract

In the context of Social TV, the increasing popularity of first and second screen users, interacting and posting content online, illustrates new business opportunities and related technical challenges, in order to enrich user experience on such environments. SAM (Socializing Around Media) project uses Social Media-connected infrastructure to deal with the aforementioned challenges, providing intelligent user context management models and mechanisms capturing social patterns, to apply collaborative filtering techniques and personalized recommendations towards this direction. This paper presents the Context Management mechanism of SAM, running in a Social TV environment to provide smart recommendations for first and second screen content. Work presented is evaluated using real movie rating dataset found online, to validate the SAM's approach in terms of effectiveness as well as efficiency.