Session-based Recommendation with Transformers

Abstract

Large item catalogs and constantly changing preference trends make recommendations a critically important component of every fashion e-commerce platform. However, since most users browse anonymously, historical preference data is rarely available and recommendations have to be made using only information from within the session. In the 2022 ACM RecSys challenge, Dressipi released a dataset with 1.1 million online retail sessions in the fashion domain that span an 18-month period. The goal is to predict the item purchased at the end of each session. To simulate a common production scenario all sessions are anonymous and no previous user preference information is available. In this paper, we present our approach to this challenge. We leverage the Transformer architecture with two different learning objectives inspired by the self-supervised learning techniques to improve generalization. Our team, LAYER 6, achieves strong results placing 2’nd on the final leaderboard out of over 300 teams.