Next-basket recommendation considers the problem of recommending a set of items into the next basket that users will purchase as a whole. In this paper, we develop a novel mixed model with preferences, popularities and transitions (M2) for the next-basket recommendation. This method models three important factors in next-basket generation process: 1) users' general preferences, 2) items' global popularities and 3) transition patterns among items. Unlike existing recurrent neural network-based approaches, M2 does not use the complicated networks to model the transitions among items, or generate embeddings for users. Instead, it has a simple encoder-decoder based approach (ed-Trans) to better model the transition patterns among items. We compared M2 with different combinations of the factors with 5 state-of-the-art next-basket recommendation methods on 4 public benchmark datasets in recommending the first, second and third next basket. Our experimental results demonstrate that M2 significantly outperforms the state-of-the-art methods on all the datasets in all the tasks, with an improvement of up to 22.1%. In addition, our ablation study demonstrates that the ed-Trans is more effective than recurrent neural networks in terms of the recommendation performance. We also have a thorough discussion on various experimental protocols and evaluation metrics for next-basket recommendation evaluation.
翻译:下一个篮子建议考虑向下一个篮子建议一组项目供用户整体购买的问题。 在本文中,我们为下一个篮子建议开发了一种与偏好、普及和过渡(M2)相混合的新模式。这个方法模型在下一代篮子生成过程中的三个重要因素:1)用户的一般偏好,2项全球流行,3项项目之间的过渡模式。与现有的经常性基于神经网络的方法不同,M2没有使用复杂的网络来模拟各项目之间的过渡,也没有为用户创建嵌入器。相反,我们有一个简单的基于编码交换器的简单方法(ED-Trans)来更好地模拟各项目之间的过渡模式。我们把M2和各种因素的不同组合与5种最先进的下一个篮子建议方法相比较,在推荐第一、第二和第三篮子时采用4种公共基准数据集。我们的实验结果显示,M2大大偏离了所有任务中所有数据集中的最新方法,并改进到22.1%。此外,我们关于经常性业绩评估协议的经常性标准研究还表明,我们更彻底的网络和更彻底的实验性评估。