The online store solved the problem of increasing demand for an expanded assortment.
He asked us to improve the recommendation system for his users so that customers would be more inclined to buy new products for themselves, and not only from the niches of previous purchases.
We collected data on customers and their purchases for 2 years, formed a metric that counts the increase in unique purchases in the user's basket over the test period and began to build a recommendation system.
The most effective approach turned out to be an ensemble of a user-based approach to recommendations coupled with KNN on the product names in the receipt for each client. The forecast announced a 49% hit of recommendations in the client's future products, but this is only in theory, because recommendations affect the purchase itself.
An A/B test was conducted, which showed a statistically significant result of the growth of total sales, among which the increase in purchases falling on new products for users was 30.5%