Hybrid Models for Tackling the Cold Start Problem in Video Recommendations Algorithms

Abstract

The cold start problem in recommender systems, especially in the domain of video recommendations, arises when new users or items enter the system without sufficient historical data, leading to poor recommendation quality. Traditional methods like collaborative filtering (CF) and content-based filtering (CBF) struggle to handle such situations effectively. This paper proposes a hybrid recommendation model that integrates CF, CBF, and deep learning techniques to address the cold start problem. The model leverages user profiles, item metadata, and contextual information to improve the quality of recommendations in sparse data scenarios. A series of experiments conducted on benchmark datasets, including MovieLens and YouTube-8M, show that the proposed hybrid model significantly outperforms traditional CF and CBF models in terms of key evaluation metrics such as precision, recall, F1-score, and diversity. Particularly in cold start scenarios, the model demonstrates substantial improvement, achieving precision rates of 78%, compared to 62% in baseline models. This paper presents not only an improved methodology but also experimental validation of its effectiveness in real-world recommendation tasks.