SURVEY ON HYBRID MODELS FOR TACKLING THE COLD START PROBLEM IN VIDEO RECOMMENDATION ALGORITHMS

Abstract

The cold start problem in recommender systems is a persistent challenge, particularly in the domain of video recommendations, where new users or content items lack sufficient historical interaction data. Traditional recommendation methods such as Collaborative Filtering (CF) and Content-Based Filtering (CBF) struggle with data sparsity, leading to poor recommendation quality. This paper surveys hybrid models that integrate CF, CBF, and deep learning techniques to mitigate the cold start problem. The review explores feature augmentation, metadata utilization, and contextual learning approaches to enhance recommendation effectiveness. Empirical studies on benchmark datasets such as MovieLens and YouTube-8M indicate that hybrid models significantly improve precision, recall, and diversity metrics. This survey further examines evaluation methods, challenges, and future directions in hybrid video recommendation systems.