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Papers/Transformers with multi-modal features and post-fusion con...

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

Gabriel de Souza P. Moreira, Sara Rabhi, Ronay Ak, Md Yasin Kabir, Even Oldridge

2021-07-11Product RecommendationRecommendation SystemsSession-Based Recommendations
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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.

Results

TaskDatasetMetricValueModel
EcommerceCoveo Data Challenge DatasetF10.0748Ensemble (60 models)
EcommerceCoveo Data Challenge DatasetMRR0.2784Ensemble (60 models)
Product RecommendationCoveo Data Challenge DatasetF10.0748Ensemble (60 models)
Product RecommendationCoveo Data Challenge DatasetMRR0.2784Ensemble (60 models)

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