Towards optimization of e-commerce search and discovery

E-Commerce (E-Com) search is an emerging problem with multiple new challenges. One of the primary challenges constitutes optimizing multiple objectives involving business metrics such as sales and revenue and maintaining a discovery strategy for the site. In this paper, we formalize the e-com search problem for optimizing metrics based on sales, revenue, and relevance. We define a notion of item discoverability in search and show that learning to rank (LTR) algorithms trained with behavioral features from e-com customer interactions (eg. clicks,cart-adds, orders etc.) do not by themselves address the discoverability problem. Instead, a suitable explore-exploit framework must be integrated with the ranking algorithm. We thus construct a practical discovery strategy by keeping a few top positions for discovery and populating some of the items selected through exploration. Then, we present a few exploration strategies with low regret bounds in terms of business metrics. We conduct a simulation study with a synthetically generated dataset that represents items with different utility distribution and compares these strategies using metrics based on sales, revenue, relevance, and discovery. We find that a strategy based on adaptive submodular function based discovery framework can provide a nice balance of business metrics and discoverability compared to other strategies based on random exploration or multi-armed bandit. However, another strategy, based on monotonic submodular optimization function that needs to be integrated with linear LTR models also works well for discovery and has nice performances with respect to sales, revenue, and relevance.