In this paper, we propose a novel model named DemiNet (short for DEpendency-Aware Multi-Interest Network) to address the above two issues. To be specific, we first consider various dependency types between item nodes and perform dependency-aware heterogeneous attention for denoising and obtaining accurate sequence item representations. Secondly, for multiple interests extraction, multi-head attention is conducted on top of the graph embedding. To filter out noisy inter-item correlations and enhance the robustness of extracted interests, self-supervised interest learning is introduced to the above two steps. Thirdly, to aggregate the multiple interests, interest experts corresponding to different interest routes give rating scores respectively, while a specialized network assigns the confidence of each score. Experimental results on three real-world datasets demonstrate that the proposed DemiNet significantly improves the overall recommendation performance over several state-of-the-art baselines. Further studies verify the efficacy and interpretability benefits brought by the fine-grained user interest modeling.

翻译：本文提出了一种名为DemiNet（依赖感知多兴趣网络）的新模型，旨在解决上述两个问题。具体而言，我们首先考虑物品节点之间的多种依赖类型，并执行依赖感知的异质注意力机制以实现降噪并获取准确的序列物品表示。其次，为提取多重兴趣，在图嵌入基础上应用多头注意力机制。为过滤项目间噪声相关性并增强所提取兴趣的鲁棒性，在上述两个步骤中引入自监督兴趣学习。第三，为聚合多重兴趣，对应不同兴趣路径的兴趣专家分别给出评分，同时由专用网络分配各评分的置信度。在三个真实数据集上的实验结果表明，所提出的DemiNet相比于多个最先进基线模型显著提升了整体推荐性能。进一步研究验证了细粒度用户兴趣建模带来的有效性与可解释性优势。