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 new mixed model with preferences and hybrid transitions for the next-basket recommendation problem. This method explicitly models three important factors: 1) users' general preferences; 2) transition patterns among items and 3) transition patterns among baskets. We compared this method with 5 state-of-the-art next-basket recommendation methods on 4 public benchmark datasets. Our experimental results demonstrate that our method significantly outperforms the state-of-the-art methods on all the datasets. We also conducted a comprehensive ablation study to verify the effectiveness of the different factors.

中文翻译：

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M2pht：用于下一篮子推荐的具有偏好和混合转换的混合模型

下一个购物篮推荐考虑将一组商品推荐到用户整体购买的下一个购物篮中的问题。在本文中，我们为下一个购物篮推荐问题开发了一个具有偏好和混合转换的新混合模型。该方法明确地模拟了三个重要因素：1) 用户的一般偏好；2）项目之间的转换模式和 3）篮子之间的转换模式。我们在 4 个公共基准数据集上将此方法与 5 个最先进的 next-basket 推荐方法进行了比较。我们的实验结果表明，我们的方法在所有数据集上都明显优于最先进的方法。我们还进行了全面的消融研究，以验证不同因素的有效性。