A Transformer-Based Multi-Domain Recommender System for E-commerce

IF 0.3 Q4 MATHEMATICS, APPLIED
Victor Giovanni Morales-Murillo, David Pinto, Fernando Pérez-Téllez, Franco Rojas-Lopez
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引用次数: 0

Abstract

Recommender systems are one of the most critical applications of AI, data science, and advanced analytics techniques because it has become integrated into our daily lives. Additionally, it serves as a powerful tool for making informed, effective, and efficient decisions and choices across a wide range of items. However, traditional techniques such as content-based and collaborative filtering often fail to consider the dynamic and short-term preferences of users when generating recommendations. To address this limitation, this research focuses on a session-based recommendation task using an XLNet transformer with various training strategies based on language modeling. Moreover, a dataset containing 102 million reviews of Amazon products was pre-processed to create two new datasets, one for a single domain and another for multi-domain data. A comparison between a GRU and the training strategies of XLNet reveals that the best training strategy achieves a 136.23% improvement in NDCG@20 and a 95.69% increase in Recall@20 for multi-domain data. In a single domain, it achieves a 168.81% improvement in NDCG@20 and a 25% increase in Recall@10.
基于变换器的电子商务多域推荐系统
推荐系统是人工智能、数据科学和高级分析技术最重要的应用之一,因为它已融入我们的日常生活。此外,它还是一种强大的工具,可用于在广泛的项目中做出明智、有效和高效的决策和选择。然而,基于内容和协同过滤等传统技术在生成推荐时往往无法考虑用户的动态和短期偏好。为解决这一局限性,本研究将重点放在基于会话的推荐任务上,使用 XLNet 转换器和各种基于语言建模的训练策略。此外,还对包含 1.02 亿条亚马逊产品评论的数据集进行了预处理,以创建两个新的数据集,一个是单领域数据集,另一个是多领域数据集。对 GRU 和 XLNet 的训练策略进行比较后发现,对于多域数据,最佳训练策略的 NDCG@20 提高了 136.23%,Recall@20 提高了 95.69%。在单域数据中,GRU 的 NDCG@20 提高了 168.81%,Recall@10 提高了 25%。
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