Representation learning-based recommendation models play a dominant role among recommendation techniques. However, most of the existing methods assume both historical interactions and embedding dimensions are independent of each other, and thus regrettably ignore the high-order interaction information among historical interactions and embedding dimensions. In this paper, we propose a novel representation learning-based model called COMET (COnvolutional diMEnsion inTeraction), which simultaneously models the high-order interaction patterns among historical interactions and embedding dimensions. To be specific, COMET stacks the embeddings of historical interactions horizontally at first, which results in two "embedding maps". In this way, internal interactions and dimensional interactions can be exploited by convolutional neural networks (CNN) with kernels of different sizes simultaneously. A fully-connected multi-layer perceptron (MLP) is then applied to obtain two interaction vectors. Lastly, the representations of users and items are enriched by the learnt interaction vectors, which can further be used to produce the final prediction. Extensive experiments and ablation studies on various public implicit feedback datasets clearly demonstrate the effectiveness and rationality of our proposed method.

翻译：基于表示学习的推荐模型在推荐技术中占据主导地位。然而，现有的大多数方法假设历史交互和嵌入维度相互独立，因此遗憾地忽略了历史交互与嵌入维度之间的高阶交互信息。本文提出了一种名为COMET（卷积维度交互）的新型基于表示学习的模型，该模型同时建模历史交互与嵌入维度的高阶交互模式。具体而言，COMET首先将历史交互的嵌入向量水平堆叠，由此生成两个"嵌入图"。通过这种方式，不同尺寸的卷积神经网络（CNN）核可以同时挖掘内部交互与维度交互。随后应用全连接多层感知机（MLP）获取两个交互向量。最后，学习到的交互向量增强了用户和物品的表示，进而可用于生成最终预测。在多种公开隐式反馈数据集上的大量实验与消融研究，清晰证明了所提方法的有效性与合理性。