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Technology
8 min read

PlayStation Plus + AI: The Future of Personalized Game Recommendations

From Generic Carousels to Adaptive Journeys

PS Plus has rich catalogs, but carousels are often static. AI-driven ranking can tailor picks per cohort and session intent.

Signals That Matter

  • Session intent: Quick play vs. weekend deep-dive.
  • Device context: Console vs. remote play; controller vs. handheld.
  • Social graph: Party history, co-op preferences, voice channel proximity.
  • Catalog state: New drops, expiring titles, genre gaps.

Model Architecture

  • Retrieval: Two-tower model with embeddings for users, games, and sessions.
  • Rerank: XGBoost/LightGBM with price sensitivity, backlog pressure, and time-to-fun.
  • Explore/Exploit: Thompson sampling to safely test new titles.

Churn-Aware Offers

LTV and churn models gate discounts and trial lengths. High-risk users see tailored win-back offers; low-risk users see discovery-focused surfacing.

Safety, Trust, and Controls

  • Age gating and content ratings respected in ranking.
  • Explainability snippets: “We picked this for your co-op streak.”
  • Opt-outs and data portability for regulatory compliance.

Impact

Expect higher session starts, lower churn, and better attachment to PS Plus Extra/Premium without overwhelming players.

Tags

PlayStation PlusGame RecommendationsRecommender SystemsAI PersonalizationSonyVector SearchLTV Modeling
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TensorBlue Gaming AI Lab

Personalization specialists focused on subscriptions, recommender systems, and player lifetime value.