Learning to rank para busca em Comércio Eletrônico

Abstract

Machine learning (ML) based ranking functions generating methods have been broadly used on web search systems, such as the utilized by Google and Bing. Nonetheless, such methods have not been employed or studied in other contexts. It is the case, to cite an example, of electronic commerce (e-commerce), on which the user interaction with virtual stores produces data as: when an user landed on a page for the first time, queries submitted, products clicked and what she bought. In this work, we propose to leverage ML to learn ranking functions for the e-commerce context. We studied alternatives to estimate the relevance of a result for a given query and deployed experiments using data mined from e-commerce shops. We ran experiments in setups we denominated offline, where a dataset was created the traditional way by separating it in three subsets of training, validation and test, as well as in setups we denominated online, where distinct versions of the system were deployed to shops facing users in a real purchase situation. We present in the study our conclusions regarding the performed experiments.