  This study proposes a novel video recommendation approach that leverages
implicit user feedback in the form of viewing percentages and social network
analysis techniques. By constructing a video similarity network based on user
viewing patterns and computing centrality measures, the methodology identifies
important and well-connected videos. Modularity analysis is then used to
cluster closely related videos, forming the basis for personalized
recommendations. For each user, candidate videos are selected from the cluster
containing their preferred items and ranked using an ego-centric index that
measures proximity to the user's likes and dislikes. The proposed approach was
evaluated on real user data from an Asian video-on-demand platform. Offline
experiments demonstrated improved accuracy compared to conventional methods
such as Naive Bayes, SVM, decision trees, and nearest neighbor algorithms. An
online user study further validated the effectiveness of the recommendations,
with significant increases observed in click-through rate, view completion
rate, and user satisfaction scores relative to the platform's existing system.
These results underscore the value of incorporating implicit feedback and
social network analysis for video recommendations. The key contributions of
this research include a novel video recommendation framework that integrates
implicit user data and social network analysis, the use of centrality measures
and modularity-based clustering, an ego-centric ranking approach, and rigorous
offline and online evaluation demonstrating superior performance compared to
existing techniques. This study opens new avenues for enhancing video
recommendations and user engagement in VOD platforms.
