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AI auto-selection of games by interests
Introduction: Matching is about appropriateness, not pressure
AI-automatic selection of games by interests helps the player to find "his" faster: theme, pace, mechanics, visual style. It does not change the mathematics of games and does not manipulate chances - it only determines the order of display and formats of prompts. The main thing is appropriateness, transparency and respect for well-being (RG).
1) Signals: what is the understanding of interests based on
Session context: device, network, language/locale, orientation, one-handed mode.
Product behavior: time to first meaningful action (TTFP), path depth, search → start → return trajectories.
Content history: favorite topics (mythology/fruits/cyberpunk), providers, mechanics (Megaways/cluster), tolerance to volatility (by aggregates).
Unloved patterns: fast failures after loading, low session depth, complaints about the interface or topic.
Quality of experience: download speed/stability, FPS/crash, "heavy" assets on mobile.
RG/ethics signals (aggregates): night marathons, lead cancellations, impulsive overbets - used for caring, not for selling.
Principles: PII minimization, explicit consent to personalization, local/federated processing where possible.
2) Fici: making "taste" measurable
Game embeddings: themes, mechanics, tempo, studio, audio/visual tags → game vector.
Player's embeddings: averaging/weighting over recent starts, "taste vectors" with exponential fading.
Co-play/co-view: games that often follow each other in sessions of similar players.
Quality factor: the probability of a quick, error-free download on the user's device.
Scenario tags: "beginner," "return," "researcher," "sprinter" (quick action).
Fairness features: restrictions on overexposure of "tops," quotas of studios/themes.
3) Model stack of auto selection
Candidate Generation (recall): ANN/embeddings + popularity in the segment → 100-300 relevant candidates.
Learning-to-Rank: Boosts/Neural Runners with Multi-Body Function (CTR @ k, "quick first experience," returns) and penalties for poor download quality/overheating.
Sequence models: Transformer/RNN predicts the next appropriate step taking into account the trajectory.
Contextual bandits: a quick online search of the shelf order within the guard metrics.
Uplift models: to whom the personal shelf really helps, and to whom the "quiet" mode/help is better.
Probability Calibration: Platt/Isotonic to match confidence with reality in new markets/devices.
4) Window orchestrator: "zel ./yellow ./Red."
Green: high confidence, low risk → personal shelves ("Looks like X," "Fast start," "Continue yesterday").
Yellow: doubt/weak network → simplified layout, easy games, less media.
Red (RG/compliance): signs of overheating/intention to "output" → the promo is hidden, the "quiet" mode is turned on, the status of payments and guides by limits are shown.
Card speed = 'relevance × quality × diversity × RG-mask'.
5) UI and explainability of recommendations
Explanation of the "why": "Looks like your recent themes," "Loads fast on your device," "New provider in your favorite mechanics."
Diversification: a mix of familiar and new topics (serendipity), quotas for the "long tail."
Honest offer cards: if there is a promo - all conditions on one screen (bet/term/wagering/cap), without "small print."
User control: "Show fewer of them," "Hide provider," toggle switch "reduce personalization."
6) What the system fundamentally does not
Does not change RTP/odds or predict the outcome of game rounds.
Does not use RG signals for pressure - only for caring mode.
Does not personalize legally relevant text and rules.
Does not apply "dark patterns" (deception timers, hidden conditions).
7) Privacy, fairness and compliance
Layer consents: showcase ≠ marketing mailings.
Data minimization: tokenization, short TTL, storage localization.
Fairness audits: no distortions by device/language/region; studio/theme exposure control.
Policy-as-Code: jurisdictional restrictions, age limits, dictionaries of permissible formulations - in the code of the orchestrator.
8) Metrics that really matter
UX-rate: TTFP, one-action-one-solution fraction.
Selection by interest: CTR @ k, "returns to titles," Depth-per-Session, completed "first experiments."
Uplift: increment of hold/returns vs control, proportion of "useful" prompts.
Quality/stability: p95 game downloads, error-rate providers, share of auto-retrays.
RG/ethics: voluntary limits/pauses, reduced night-time overheating, zero substantiated complaints.
Fairness/ecosystem: showcase variety (Gini/Entropy), share "long tail" in top cards.
9) Reference architecture
Event Bus → Feature Store (online/offline) → Candidate Gen (ANN/embeddings) → Ranker (LTR/seq/uplift + calibration) → Policy Engine (zel/yellow/red, fairness, compliance) → UI Runtime (shelves/cards/explanations) → XAI & Audit → Experimentation (A/B/bandits/geo-lift) → Analytics (KPI/RG/Fairness/Perf)
In parallel: Content catalog (game metadata), Quality Service (download/errors), Privacy Hub (consent/TTL), Design System (A11y tokens).
10) Operational scenarios
New user: recall on light topics + "quick start"; explanation "for your network."
Return after pause: "Continue" + 1-2 fresh topics; the bandit determines the order.
Weak network/low battery: orchestrator includes light media mode; The quality factor moves the cards up.
Intention "conclusion": the showcase hides the promo, shows the statuses "instantly/check/manual verification" and guide "how to speed up."
Provider failure: quality-score drop → automatic title replacement and XAI cause marking.
11) Experiments and "careful" bandits
Guard metrics: errors/complaints/RG - automatic rollback on degradation.
A/A and shadow rollouts: check stability before switching on.
Uplift tests: we measure the increment, not just the CTR.
Adaptation kapping: no more than N order changes per session; understandable "rollback to default."
12) MLOps and operation
Versioning of dates/features/models/thresholds; full lineage.
Flavor/channel/device drift monitoring; auto-calibration of thresholds.
Feature flags and fast rollback; sandboxes for regulator and internal audits.
Test packs: performance (LCP/INP), A11y (contrast/focus), compliance (forbidden formulations).
13) Implementation Roadmap (8-12 weeks → MVP; 4-6 months → maturity)
Weeks 1-2: event dictionary, games catalog, Privacy Hub/consent, basic recall.
Weeks 3-4: LTR v1 with quality factors, fast start mode, XAI explanations.
Weeks 5-6: seq-models of paths, bandits, fairness-quotas, policy-as-code.
Weeks 7-8: uplift models, RG-guardrails, perf optimization, shadow rolling.
Months 3-6: federated processing, autocalibration, market scaling, regulatory sandboxes.
14) Frequent mistakes and how to avoid them
Optimize CTR only. Add quick experience, hold, and uplift goals.
Overexpose hits. Include diversity/fairness quotas and serendipity.
Ignore download quality. Quality-score is required in ranking.
There is no explainability. Show "why recommended" and give control ("fewer such").
Mix RG and promo. With overheating signals - silence promo, help and limits.
Fragile releases. Feature flags, A/A, quick rollback - otherwise you risk "dropping" the funnel.
AI auto-fit games are a system of appropriateness: clean signals, calibrated models, rules of care, and an explainable interface. Such an outline speeds up the search for "your" content, maintains a healthy ecosystem and builds trust. The formula is simple: data → recall/rank/seq/uplift → policy-engine → transparent UI. Then the showcase feels "yours," and the product feels honest, fast and comfortable.