Comprehensive Overview of Modern Recommendation System Technologies
This article presents a detailed survey of recent advances in recommendation system technology, covering system architecture, user understanding layers, various recall methods, ranking techniques, auxiliary algorithms such as cold-start and bias modeling, and evaluation metrics, with references to industry practices and academic research.
The presentation outlines the overall development of recommendation systems, beginning with a two‑layer architecture that separates data processing (real‑time collection, feature engineering, and storage) from model serving (offline training, online deployment using frameworks like TensorFlow, PyTorch, or Alibaba PAI).
User understanding is divided into three hierarchical layers: data, insight, and comprehension, each incorporating explicit (behavior filtering, labeling, clustering) and implicit (vector representations, multi‑modal modeling) approaches.
Recall techniques are categorized into engineering side (high‑performance multi‑channel pipelines) and algorithmic side, which includes traditional, knowledge‑graph, representation‑based, and matching‑based recall, with examples such as SVD, UserCF, Graph‑based models, and recent methods like TDM and DR.
Ranking technology progresses from coarse‑ranking (rule‑based, linear models, dual‑tower, AutoFAS) to fine‑ranking, evolving from LR and FM to deep models (MLP, attention, GNN, DIN, DIEN) and multi‑task/multi‑objective/multi‑modal frameworks (MMOE, PLE, MMGCN, LOGO).
Additional recommendation directions cover cold‑start strategies, bias modeling (position, exposure, popularity), and explainability, emphasizing the need for transparent recommendations across user, item, text, visual, and social contexts.
The evaluation section reviews both overall metrics (CTR, CVR, ECPM, diversity, novelty) and stage‑specific metrics for recall, coarse‑ranking, fine‑ranking, and re‑ranking, highlighting the importance of combining online A/B testing with offline analysis.
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