Personalization in marketing uses AI-powered recommendation systems to tailor content, products, and messages to individual users. This paper provides an analytical, rigorous survey of AI personalization, covering definitions and scope; historical evolution; core algorithms and architectures (collaborative filtering, content-based, hybrid, matrix factorization, factorization machines, deep learning, sequence models like SASRec/BERT4Rec, graph-based models such as LightGCN, reinforcement learning, and emerging causal approaches); data sources and feature engineering (multi-modal data, feature stores, augmentation); evaluation metrics (accuracy, ranking, diversity, novelty, serendipity, calibration, fairness, business KPIs like CTR, conversion, retention); system design and deployment (offline vs real-time pipelines, scalability, latency, A/B testing, online learning, MLOps); personalization strategies across channels (email, web, mobile, ads, in-store); case studies of major companies (Amazon, Netflix, Spotify, Google/YouTube, TikTok) and varied industries; and privacy/ethics/regulation concerns (GDPR, CCPA, differential privacy, federated learning, transparency, explainability, bias mitigation). We include tables comparing algorithms and metrics, mermaid diagrams (timeline and pipeline), and charts where relevant. The paper concludes with actionable recommendations and open research questions. All claims are supported by recent (2021–2026) academic and industry sources.

AI-powered personalization transforms marketing by delivering relevant content and boosting business outcomes. We have covered the spectrum from classic CF to cutting-edge neural and causal models. Based on our survey: •Business Implementation: Start with user-item interaction data and simple collaborative filters, then incrementally add content features and deep models. Use offline metrics (NDCG, diversity scores) for initial development, but validate with online A/B tests on CTR, conversion, and retention. Invest in MLOps: automated pipelines for retraining, monitoring, and deployment are essential for scalability and reliability. •Data Strategy: Emphasize data quality. Use multi-modal features (images, text) extracted via pretrained networks. Employ data augmentation and maintain a feature store for consistency. Consider data-centric approaches to improve logs and labels. •Algorithm Choice: Align algorithms to use case. For example, if content side info is rich, include content-based or hybrid approaches. Use sequence or reinforcement models for sequential/interactive scenarios. Always tune algorithms for business KPIs, not just accuracy. •Privacy and Ethics: Comply with regulations by design: anonymize data, allow opt-out, and consider federated learning. Regularly audit for bias and fairness. Incorporate explainability (e.g. “why recommended”) to build user trust. •Future Development: Explore causal methods to mitigate biases and measure true impact. Pilot LLM-based recommendation experiments for personalized content generation. Keep an eye on federated/personalization privacy research.

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