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.

翻译：以会议为基础的建议是电子商务服务的一项重要任务,在电子商务服务中,许多用户匿名浏览,或可能对不同会议有截然不同的兴趣;在本文件中,我们介绍了SIGIR 2021电子商务数据挑战问题研讨会的建议任务的一个获奖解决办法;我们的解决方案是受NLP技术的启发,由两种变革型结构(变压器-XL和XLNet)组成的组合组成,经过自动递减和自动编码方法的培训;为了利用为竞争提供的大部分丰富的数据集,我们介绍了我们如何通过将表格活动与文字和图像矢量相结合的方式制作多模式特征;我们还提出了模型预测分析,以更好地了解我们会议建议的结构的有效性。