> ML_RECIPE // REALTIME-ECOMMERCE-SESSION-RECOMMENDATIONS_v1.0
Real-Time E-Commerce Session & Next-Item Recommendation
Recommend personalized related products based on in-session click sequences in < 15ms, boosting cart additions and cross-sell revenue while maintaining strict user privacy boundaries.
Business Outcome
Recommend personalized related products based on in-session click sequences in < 15ms, boosting cart additions and cross-sell revenue while maintaining strict user privacy boundaries.
Personalized recommendations must show statistically significant lift in CTR (+15%) over category bestsellers in A/B test.
Heuristic Baseline
Category bestsellers: Display top 10 most purchased items in the currently viewed category over the past 7 days.
Category bestsellers achieved HitRate@10 of 0.14 with poor coverage of long-tail catalog inventory.
Phase 1: Prototype Path
Train Alternating Least Squares (ALS) model on 90 days of implicit user click/purchase interactions. Evaluate HitRate@10 and MAP@10.
Phase 2: Production Path
Export item embedding factors to vector cache. Query nearest neighbor vector index from API edge worker with Redis session click history.
Compute & Placement Topologies
Nightly batch training job on multi-core CPU or single GPU server
In-memory vector lookup microservice (Redis cache) running on API server CPU
3-Plan Placement Alternatives
Nightly precomputed item-item top 20 recommendations stored in PostgreSQL JSONB column.
In-memory nearest-neighbor index loaded into Python FastAPI service on standard 8-core CPU server.
Nightly ALS training on GPU/CPU cluster + streaming real-time click session vector updates + in-memory index with Redis cache (< 10ms P99 latency).
Recommended Libraries & Tools
Governance, Safeguards & Risks
- Privacy & behavioral profiling boundary: Comply with GDPR/KVKK consent frameworks. Support anonymous in-session contextual recommendations without persistent cross-site tracking or profiling cookies.
- Diversity & stock constraints: Enforce catalog diversity filters preventing duplicate variant spam and immediately filter out out-of-stock SKUs.
- Age-gating safeguard: Strictly suppress adult, tobacco, alcohol, or age-restricted merchandise from generic recommendation feeds.