Our Recommend algorithm uses artificial intelligence to train the models. The Frequently Bought Together model will recommend items that were bought together and some that might go well together. For example, if item A was was bought with item B, and item B was bought with item C, the model might learn to recommend item C to A or item A to C.
Furthermore, if you are using an "add to cart" conversion event, it is possible that some noise will appear if a user adds an item to cart but does not complete the purchase. These events will still be considered in the Recommend model. However, it’s possible for users using Frequently Bought Together model to select between two variants:
- Relaxed (default) - This is our collaborative filtering approach. It utilizes past purchases to recommend items likely to complement each other through inferred relationships, to increase catalog discovery. For instance, if users are buying A+B and B+C, the model can suggest C to A even if they haven’t been bought together before.
- Strict - Only recommend items that have been purchased together in the past. It means also that fewer recommendations are generated.