CONCIERGE: Improving Constrained Search Results by Data Melioration

The problem of finding an item-set of maximal aggregated utility that satisfies a set of constraints is at the cornerstone of many e-commerce applications. Its classical definition assumes that all the information needed to verify the constraints is explicitly given. In practice, however, the data available in e-commerce databases on the items is often partial. Hence, adequately answering constrained search queries requires the completion of this missing information. A common approach to complete missing data is to employ Machine Learning (ML) algorithms. However, ML is naturally error-prone. More accurate data can be obtained by asking the items’ sellers to complete missing data. But as the number of items in the repository is huge, asking sellers about all items is prohibitively expensive. CONCIERGE, our presented system, assists the e-commerce platform in identifying a bounded-size set of items whose data should be manually completed, as these items are expected to contribute the most to the constrained search queries in question. We demonstrate the effectiveness of our system on real-world data and scenarios taken from a large e-commerce system by interacting with the VLDB’20 participants who act as both analysts and the sellers.