Part 3/12:
While reward-based suggestions are relatively straightforward — since the system knows the customer's preferences based on past behavior — acquisition recommendations are far trickier. The main challenge lies in the lack of prior information about the customer’s affinity to new products and the vastness of the product catalog, which can range from 1,000 to 2 million items. Considering that most customers purchase only 4–500 products annually, the data landscape becomes highly sparse, requiring more sophisticated modeling techniques to explore uncharted preferences effectively.