College of Mathematics and Informatics, South China Agricultural University, Guangzhou, China.
College of Mathematics and Informatics, South China Agricultural University, Guangzhou, China; Wens Foodstuff Group Co., Ltd., Yunfu, Guangdong, China.
Neural Netw. 2024 Dec;180:106675. doi: 10.1016/j.neunet.2024.106675. Epub 2024 Sep 2.
The next basket recommendation task aims to predict the items in the user's next basket by modeling the user's basket sequence. Existing next basket recommendations focus on improving recommendation performance, and most of these methods are black-box models, ignoring the importance of providing explanations to improve user satisfaction. Furthermore, most next basket recommendation methods are designed for consumer users, and few methods are proposed for business user characteristics. To address the above problems, we propose a Knowledge Reinforced Explainable Next Basket Recommendation (KRE-NBR). Specifically, we construct a basket-based knowledge graph and obtain pretrained embeddings of entities that contain rich information of the knowledge graph. To obtain high-quality user predictive vectors, we fuse user pretrained embeddings, user basket sequence level embeddings, and user repurchase embeddings. One highlight of the user repurchase embeddings is that they are able to model business user repurchase behavior. To make the results of next basket recommendations explainable, we use reinforcement learning for path reasoning to find the items recommended in the next basket and generate recommendation explanations at the same time. To the best of our knowledge, this is the first work to provide recommendation explanations for next basket recommendations. Extensive experiments on real datasets show that the recommendation performance of our proposed approach outperforms several state-of-the-art baselines.
下一个篮子推荐任务旨在通过对用户篮子序列进行建模来预测用户下一个篮子中的项目。现有的下一个篮子推荐方法主要集中在提高推荐性能上，这些方法大多是黑盒模型，忽略了提供解释以提高用户满意度的重要性。此外，大多数下一个篮子推荐方法是针对消费者用户设计的，很少有针对商业用户特征的方法。为了解决上述问题，我们提出了一种知识增强可解释的下一个篮子推荐（KRE-NBR）。具体来说，我们构建了一个基于篮子的知识图，并获得了包含丰富知识图信息的实体的预训练嵌入。为了获得高质量的用户预测向量，我们融合了用户预训练嵌入、用户篮子序列级别嵌入和用户回购嵌入。用户回购嵌入的一个亮点是，它们能够对商业用户的回购行为进行建模。为了使下一个篮子推荐的结果具有可解释性，我们使用强化学习进行路径推理，以找到下一个篮子中推荐的项目，并同时生成推荐解释。据我们所知，这是第一个为下一个篮子推荐提供推荐解释的工作。在真实数据集上的广泛实验表明，我们提出的方法的推荐性能优于几种最先进的基线。
