Building an AI Product Recommendation Engine

Instrument your platform to capture: product views, searches, add-to-cart, purchases, ratings, and dwell time. Build a unified customer profile combining behavioural data with demographic and transactional history. Address cold-start for new users and new products. Implement event tracking with a consistent schema from day one; retrofitting event taxonomy later is extremely costly. Capture implicit signals (dwell time, scroll depth) alongside explicit signals (add-to-cart, purchase) since implicit data is 10-50x more abundant and smooths cold-start issues.

Implement multiple recommendation approaches: collaborative filtering (users with similar behaviour), content-based filtering (similar product attributes), and hybrid models. Use deep learning embeddings for complex pattern recognition. Build A/B testing framework for model comparison. Start with a matrix-factorisation baseline (ALS) before investing in deep-learning approaches; it trains in minutes and provides a strong benchmark. Use offline metrics (NDCG, recall@K) for model selection but always validate with online A/B tests since offline improvements do not always translate to revenue lift.

Deploy recommendation API capable of sub-100ms response times. Implement feature store for real-time customer context. Build caching layer for frequently requested recommendations. Design fallback strategies for cold-start scenarios. Cache the top 100 recommendations per user in Redis with a 15-minute TTL to handle traffic spikes without overloading the model server. Implement a fallback chain: user-personalised first, then segment-level, then popularity-based, ensuring every request returns recommendations even during model downtime.

Add recommendations to product pages, homepage, search results, email campaigns, and app push notifications. Design UI components that maximise recommendation visibility and click-through. Implement business rules (inventory, margin, promotion) as recommendation filters. Tailor recommendation logic by placement: homepage should emphasise discovery and diversity, product pages should show similar and complementary items, and cart pages should focus on accessories and bundles. A/B test widget placement and the number of items shown per carousel.

Run continuous A/B tests on recommendation algorithms, placement, and UI. Implement contextual bandits for real-time optimisation. Add diversity and serendipity controls to avoid filter bubbles. Track revenue attribution to recommendations. Implement a diversity parameter (typically 0.2-0.4) that blends exploration with exploitation to prevent filter bubbles and surface long-tail products. Track catalogue coverage (percentage of products recommended at least once per week) as a health metric alongside CTR and revenue.