MSR: A Personalized Movie Recommendation Model Based on Gate Mechanism and Attention Network

Personalized recommendation systems play a crucial role in alleviating information overload and satisfying users’ specific preferences. To address the challenges of inadequate user historical data extraction and the cold start problem inherent in traditional movie recommendation systems, we present a novel personalized movie recommendation model known as “movie recommendation with starring roles and ratings” (MSR). By incorporating a multi-head attention mechanism, the model captures intricate relationships among diverse data fields within users’ viewing records and facilitates the extraction of user features through the basic information-rating joint attention network (BRJA). The gate mechanism efficiently integrates fundamental movie information and average score into the movie representation vector, thereby generating candidate movie features. MSR can effectively provide recommendations even when confronted with limited user information, effectively mitigating the cold start problem. Comparative experiments on the movie lens dataset and ablation experiments focusing on key modules demonstrate the effectiveness of MSR in improving movie recommendations.