دانلود کتاب پیشرفت با گراف سیستم های توصیه گر شبکه های کانولوشن

Recommender systems have become an indispensable component of modern online platforms, shaping user experiences by offering personalized suggestions that enhance satisfaction, increase engagement, and drive business success. From streaming services to e-commerce platforms, these systems leverage vast amounts of data to anticipate user needs and provide tailored content. Among the many advancements in this domain, Graph Convolutional Networks (GCNs) have emerged as a transformative tool. By effectively modeling graph structures, GCNs excel in capturing high-order relationships between users and items, enabling the creation of enriched and expressive representations. However, despite their promise, both GCN-based methods and recommender systems at large face critical challenges that constrain their scalability, effectiveness, and adoption. This book systematically examines and addresses these challenges, categorized into two overarching themes. The first focuses on problems specific to GCN-based recommendation models, including: 1. Over-smoothing Problem: A pervasive problem where user and item embeddings converge to indistinguishable representations as GCN layers deepen, diminishing personalization and recommendation quality. 2. Noisy Neighboring Nodes Issue: GCN models often aggregate information indiscriminately from all high-order neighbors, potentially amplifying irrelevant or unreliable signals, thereby compromising the quality of recommendations. 3. Interpretability limitations: The intricate aggregation processes in GCNs obscure the reasoning behind recommendations, reducing transparency and trust in critical applications.