People usually have different intents for choosing items, while their preferences under the same intent may also different. In traditional collaborative filtering approaches, both intent and preference factors are usually entangled in the modeling process, which significantly limits the robustness and interpretability of recommendation performances. For example, the low-rating items are always treated as negative feedback while they actually could provide positive information about user intent. To this end, in this paper, we propose a two-fold representation learning approach, namely Double Disentangled Collaborative Filtering (DDCF), for personalized recommendations. The first-level disentanglement is for separating the influence factors of intent and preference, while the second-level disentanglement is performed to build independent sparse preference representations under individual intent with limited computational complexity. Specifically, we employ two variational autoencoder networks, intent recognition network and preference decomposition network, to learn the intent and preference factors, respectively. In this way, the low-rating items will be treated as positive samples for modeling intents while the negative samples for modeling preferences. Finally, extensive experiments on three real-world datasets and four evaluation metrics clearly validate the effectiveness and the interpretability of DDCF.

翻译：人们通常对选择物品持有不同的意图，而同一意图下的偏好也可能存在差异。在传统协同过滤方法中，意图与偏好因素在建模过程中通常纠缠在一起，这严重限制了推荐性能的鲁棒性和可解释性。例如，低评分物品常被视为负反馈，但实际上它们可能提供了关于用户意图的正面信息。为此，本文提出了一种双重表征学习方法，即双重解耦协同过滤（DDCF），用于个性化推荐。第一层解耦旨在分离意图与偏好的影响因素，而第二层解耦则在有限计算复杂度下，构建每个意图对应的独立稀疏偏好表征。具体而言，我们采用两个变分自编码器网络——意图识别网络与偏好分解网络——分别学习意图和偏好因素。通过这种方式，低评分物品将被视作意图建模的正样本，同时作为偏好建模的负样本。最终，在三个真实数据集和四个评估指标上的大量实验清晰地验证了DDCF的有效性和可解释性。