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Session-based recommendation is an important task for e-commerce services,where a large number of users browse anonymously or may have very distinctinterests for different sessions. In this paper we present one of the winningsolutions for the Recommendation task of the SIGIR 2021 Workshop on E-commerceData Challenge. Our solution was inspired by NLP techniques and consists of anensemble of two Transformer architectures - Transformer-XL and XLNet - trainedwith autoregressive and autoencoding approaches. To leverage most of the richdataset made available for the competition, we describe how we preparedmulti-model features by combining tabular events with textual and imagevectors. We also present a model prediction analysis to better understand theeffectiveness of our architectures for the session-based recommendation.

Transformers with multi-modal features and post-fusion context for e-commerce session-based recommendation