Recommendation Engine

the-right-content-for-every-user

The Right Content for Every User The Recommendation Engine serves personalized content, product, and action recommendations based on user behavior, preferences, and predictive models. Collaborative filtering identifies patterns from similar users. Content-based filtering matches based on item attributes. Hybrid models combine both approaches. Recommendations are served in real-time via API and can be embedded anywhere — product pages, email campaigns, in-app messages, or custom widgets. A/B testing integration measures the lift from recommendations against non-personalized baselines.

Recommendation Engine Specifications

Algorithms

Collaborative filtering, content-based, hybrid, deep learning (neural collaborative filtering)

Latency

<20ms for recommendation retrieval

Contexts

Product pages, email, in-app, search results, custom

A/B Testing

Built-in recommendation experiment framework

Cold Start

Popularity-based fallback for new users/items

features

hybrid: true

collaborative: true

true

deep learning: true

Relationships