Design of an Iterative Method for Enhanced Recommender Systems Incorporating Hybrid Filtering, Matrix Factorization, and Deep Learning with Attention Mechanisms

Abstract

This research critically examines the pertinence of advanced recommender systems in tandem with the burgeoning ecommerce and content streaming domains. Traditional recommendation systems falter in cold-start scenarios, where sparse user or item data leads to inaccurate suggestions. Moreover, they overlook diverse interaction and auxiliary information within user-item pairs. Addressing these challenges, the paper introduces a novel hybrid recommendation system amalgamating collaborative filtering, contentbased filtering, and knowledge-based techniques. Leveraging user-item interaction data alongside item and user features, when available, enhances recommendation coverage and accuracy for new entities. Matrix factorization with side information integrates content features into collaborative filtering, enriching personalization via latent factors. Deep learning models with attention mechanisms exploit auxiliary information, refining recommendation quality dynamically. Real-time interaction and scenario data fuel a contextual bandit framework, continuously evolving user profiles via multi-armed bandit algorithms. Employing Approximate Nearest Neighbors techniques like Locality-Sensitive Hashing expedites user similarity identification, curtailing computational overhead. Finally, ensemble learning with model stacking integrates predictions from multiple recommendation models, mitigating biases and capturing diverse data patterns. The study's ramifications are extensive, notably boosting recommendation precision and recall, thereby augmenting user satisfaction and engagement significantly. By offering a holistic approach to the cold-start problem, encompassing diverse data sources and recommendation techniques, this research makes a substantial contribution to the field.