Improving Recommender Systems using Hybrid Techniques of Collaborative Filtering and Content-Based Filtering

Abstract

This innovative study introduces a novel enhancement to recommendation systems through a synergistic integration of Collaborative Filtering (CF) and Content-Based Filtering (CBF) techniques, termed the hybrid CF-CBF approach. By seamlessly amalgamating the strengths of CF's user interaction insights and CBF's content analysis prowess, this approach pioneers a more refined and personalized recommendation paradigm. The research encompassed meticulous phases, including comprehensive data acquisition, efficient storage management, meticulous data refinement, and the skillful application of CF and CBF methodologies. The findings markedly highlight the prowess of the hybrid approach in generating recommendations that exhibit enhanced diversity and precision, surpassing the outcomes obtained from either technique in isolation. Remarkably, the hybrid CF-CBF approach effectively addresses the inherent shortcomings of individual methods, such as CF's vulnerability to the "cold start" problem and CBF's limitation in fostering recommendation diversity. By fostering a harmonious synergy, this novel approach transcends these limitations and provides a holistic solution. Furthermore, the interplay of CF and CBF augments the recommender system's cognitive grasp of user preferences, subsequently enriching the quality of recommendations provided. In conclusion, this research stands as a pioneering contribution to the evolution of recommendation systems by championing the hybrid CF-CBF approach. By ingeniously fusing two distinct techniques, the study engenders a breakthrough in personalized recommendations, thereby propelling the advancement of more sophisticated and effective recommendation systems.

Enhancing Personalized Recommendations: A Study on the Efficacy of Multi-Task Learning and Feature Integration

“…Traditional approaches, such as collaborative filtering (CF) and content-based filtering, have exhibited limitations in handling sparse data matrices and the cold-start problem [1,2]. Recent advances in deep learning have catalyzed the development of neural networkbased hybrid models that synergistically combine the strengths of CF and content-based approaches [3,4]. These models learn low-dimensional embeddings and fuse them with other features through deep neural networks, enabling them to capture complex patterns and non-linear relationships.…”