Smart Recommender Algorithms

Smart Recommender can recommend products with different algorithms that match your potential customer's interests. Machine Learning powered Smart Recommender Algorithms can work with different page types and aim to increase clicks and conversions. We classify the algorithms in 4 different groups:

• Generic algorithms
• Contextual algorithms
• Collaborative algorithms
• Algorithms with multi strategies

Generic Algorithms

In Generic Algorithms, product recommendations are based on product performance rather than individual consumer behavior or the momentary actions of the user. The algorithms are:

• Most Popular Items
• Top Sellers
• Highest Discounted Products
• Manual Merchandising
• New Arrivals
• Trending Products
• Most Valuable Products 

Contextual Algorithms

In Contextual Algorithms, product recommendations are based on the current context of the user event (e.g. product category of the currently visited page), and product relations (e.g. products that are viewed or purchased together). The algorithms are:

• Viewed Together
• Purchased Together
• Location-based Top Sellers
• Checkout Recommendation
• Most Popular Items of the Category
• Most Valuable Items of the Category
• Substitute Products
• Complementary Products
• Recently viewed
• Purchased with Last Purchased

Collaborative Algorithms

In Collaborative Algorithms, product recommendations are based on behavioral patterns of similar users by taking product visits, purchases and added-to-carts into account. Recommendations include top clicked and purchased products from similar user’s events that are not visited by the current user. The algorithm is user-based recommendation.

Algorithms with Multi Strategies 

In these algorithms, more than one algorithm perform to offer related product recommendation. They are:

• Mixed Strategy
• Chef - Auto Optimization for Recommendation Algorithms