  Session-based recommendation is an important task for e-commerce services,
where a large number of users browse anonymously or may have very distinct
interests for different sessions. In this paper we present one of the winning
solutions for the Recommendation task of the SIGIR 2021 Workshop on E-commerce
Data Challenge. Our solution was inspired by NLP techniques and consists of an
ensemble of two Transformer architectures - Transformer-XL and XLNet - trained
with autoregressive and autoencoding approaches. To leverage most of the rich
dataset made available for the competition, we describe how we prepared
multi-model features by combining tabular events with textual and image
vectors. We also present a model prediction analysis to better understand the
effectiveness of our architectures for the session-based recommendation.
