MC3: A System for Minimization of Classifier Construction Cost
Shay Gershtein, Tova Milo, Gefen Morami, Slava Novgorodov · 2020
Search mechanisms over massive sets of items are the cornerstone of many modern applications, particularly in e-commerce websites. Consumers express in search queries a set of properties, and expect the system to retrieve qualifying items. A common difficulty, however, is that the information on whether or not an item satisfies the search criteria is sometimes not explicitly recorded in the repository. Instead, it may be considered as general knowledge or "hidden" in a picture/description, thereby leading to incomplete search results. To overcome these problems companies invest in building dedicated classifiers that determine whether an item satisfies the given search criteria. However, building classifiers typically incurs non-trivial costs due to the required volumes of high-quality labeled training data. In this demo, we introduce MC3, a real-time system that helps data analysts decide which classifiers to construct to minimize the costs of answering a set of search queries. MC3 is interactive and facilitates real-time analysis, by providing detailed classifiers impact information. We demonstrate the effectiveness of MC3 on real-world data and scenarios taken from a large e-commerce system, by interacting with the SIGMOD'20 audience members who act as analysts.