- 08 May 2024
- 1 Minute to read
Smart Recommender Algorithms
- Updated on 08 May 2024
- 1 Minute to read
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 into 4 different groups:
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. The recommendations include top-clicked and purchased products from events of similar users that the current user has not visited. This algorithm operates on a user-based recommendation system
Algorithms with Multi-strategies
In these algorithms, more than one algorithm performs to offer related product recommendations. They are:
- Mixed Strategy
- Chef - Auto Optimization for Recommendation Algorithms