Elo7 is the largest Brazilian marketplace for handcrafted products. Search is a key tool for a user to find products at Elo7. Hence, the main goal of our search system is to optimize relevance. Today, this is accomplished through the boost of manually specified fields inside our search retrieval engine. However, since there are many fields and many different scores, understanding the search results is not trivial. To make sure the search engine behaves as expected, with the least possible side-effects, we developed an Explainable Machine Learning model using the novel method SHAP (SHapley Additive exPlanations), which is based on cooperative game theory. The model was able to show how products features used by the search engine contribute as coalition for ranking and relevance of the retrieval set.
Short Bio
Junior A. Koch is a physicist by Universidade Federal de Santa Catarina (UFSC), where he also got his PhD. in Materials Science and Engineering. After graduating, he worked as a Professor at UFSC and Centro Universitário Católica de Santa Catarina teaching Physics, Mathematics and Programming. Five years later, he worked as a postdoc in General Relativity and Quantum Gravity. Since 2019, he is part of the data science team at Elo7, and has been applying machine learning techniques to a wide range of problems, e.g. search and recommendation.