How does Visually’s recommendation engine work?

Visually’s recommendation engine combines multiple machine learning and rule-based approaches to generate highly relevant product recommendations.

Depending on the strategy, the platform may use one or several algorithms simultaneously.

Recommendation Algorithms

• Content-Based Filtering - Recommends products based on product attributes, metadata, and similarities to user preferences.

• Association Rules - Identifies products frequently viewed or purchased together using historical behavioral patterns.

• Collaborative Filtering - Uses similarities between users and products to generate personalized recommendations.

Includes:

• User-based collaborative filtering
• Item-based collaborative filtering

Deep Learning

Neural network models trained on user interactions and behavioral signals to uncover complex recommendation patterns.