Learning to diversify for e-commerce search with multi-Armed bandit

Search is central to e-commerce platforms. Diversification of search results is essential to cater to the diverse preferences of the customers. One of the primary metrics of e-commerce businesses is revenue. On the other hand, the prices of the products shown influence customer preferences. Hence, diversifying e-commerce search results requires learning the diverse price preferences of the customers and simultaneously maximizing the revenue without hurting the relevance of the results. In this paper, we introduce the learning to diversify problem for e-commerce search. We also show that diversification improves the median customer lifetime value (CLV), which is a critical long-Term business metric for an e-commerce business. We design three algorithms for the task. The first two algorithms are modifications of algorithms that are in the past developed in the context of the diversification problem in web search. The third algorithm is a novel approximate knapsack based semi-bandit algorithm. We derive the regret and pay-off bounds of all these algorithms and conduct experiments with synthetic data and simulation to validate and compare the algorithms. We compute revenue, median CLV, and purchase based mean reciprocal rank (PMRR) under various scenarios such as with changing user preferences with time in our simulation to compare the performances of these algorithms. We show that our proposed third algorithm is more practical and efficient compared to the first two algorithms and can produce higher revenue, maintain a better median CLV and PMRR.