# Transformers with multi-modal features and post-fusion context for e-commerce session-based recommendation Source: [Transformers with multi-modal features and post-fusion context for e-commerce session-based recommendation]() Collection index: [llms.txt]() Data updated: 2026-01-04T04:08:21.970Z Authors: Gabriel de Souza P. Moreira, Sara Rabhi, Ronay Ak, Md Yasin Kabir, Even Oldridge Published: 2021-07-11T20:02:59.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Session-based recommendation on SIGIR E-commerce Data Challenge Leaderboard 2021](): Session-based recommendation; SIGIR E-commerce Data Challenge Leaderboard 2021; MRR: 27.84. ## Original sources and implementation [Original paper]() [PDF]() [Implementation]()