#Matrix Factorization

Gain insights into the fundamentals and advanced techniques of building recommender systems with Python. This guide provides an overview of content-based filtering, collaborative filtering, matrix factorization, and more using popular datasets like Kaggle's movie and movielens datasets. Discover practical implementations using deep learning with Keras, wide & deep models, and innovative methods using ChatGPT and LLM for enhanced explainability. Learn to apply these techniques to datasets such as Naver news and understand the principles behind each method, serving data scientists and developers.

Explore a curated collection of advanced community detection research papers, covering diverse methods such as matrix factorization, deep learning, and spectral analysis. This resource offers comprehensive chapters on techniques like label propagation and cyclic patterns, relevant to fields including graph classification and fraud detection. Ideal for both researchers and practitioners, it provides insights into models like centrality and block models, and discusses the latest developments in hypergraphs and physics-inspired methods.

This guide thoroughly investigates various facets of recommender systems, covering introductory concepts through complex machine learning and deep learning techniques. It includes in-depth discussions on collaborative filtering, matrix factorization, and a range of algorithms like FM, FFM, GBDT+LR, AutoRec, and DeepFM. Additionally, it explores practical implementation strategies, evaluation techniques, and engineering considerations crucial for deployment. This objective overview is an invaluable resource for professionals and researchers focused on advancing their comprehension of the architecture and practical applications of recommender systems.