Building Recommender Systems with Machine Learning and AI

Abstract

Recommender systems are a fundamental element of modern digital ecosystems, connecting users with relevant products, services, or content. This study provides a comprehensive review examining the combination of machine learning and artificial intelligence in the design of hybrid recommender systems. Three main approaches user-based collaborative filtering, content-based filtering, and hybrid models are examined in depth, comparing the strengths and limitations of each method. The review section summarizes the current state of the literature regarding scalability, cold-start issues, reliability, and evaluation metrics. Furthermore, key challenges and existing solutions in the literature are presented in a comparative manner. Next, a Python-based hybrid systems design is detailed. This systems utilizes an approach that combines matrix multiplication-based factorization techniques with similarity analysis of user metadata. Matrix factorization extracts underlying patterns from user and item interactions, while similarity calculations with user metadata improve recommendation quality. This integration allows for improved recommendation accuracy and personalization. Furthermore, findings on the integration of multiple data sources and how context differences can be addressed are shared. The results demonstrate that integrating AI-based techniques can significantly improve recommendation quality. The findings demonstrate that hybrid approaches are particularly effective for modeling complex user behaviors and mitigating challenges such as the cold start problem. The study further explores the potential future extension of hybrid models to different data sources and contexts, and discusses the applicability of optimization strategies to real-world systems.