IEEE Trans Neural Netw Learn Syst. 2022 Oct;33(10):5125-5137. doi: 10.1109/TNNLS.2021.3069058. Epub 2022 Oct 5.
In recommendation, both stationary and dynamic user preferences on items are embedded in the interactions between users and items (e.g., rating or clicking) within their contexts. Sequential recommender systems (SRSs) need to jointly involve such context-aware user-item interactions in terms of the couplings between the user and item features and sequential user actions on items over time. However, such joint modeling is non-trivial and significantly challenges the existing work on preference modeling, which either only models user-item interactions by latent factorization models but ignores user preference dynamics or only captures sequential user action patterns without involving user/item features and context factors and their coupling and influence on user actions. We propose a neural time-aware recommendation network (TARN) with a temporal context to jointly model 1) stationary user preferences by a feature interaction network and 2) user preference dynamics by a tailored convolutional network. The feature interaction network factorizes the pairwise couplings between non-zero features of users, items, and temporal context by the inner product of their feature embeddings while alleviating data sparsity issues. In the convolutional network, we introduce a convolutional layer with multiple filter widths to capture multi-fold sequential patterns, where an attentive average pooling (AAP) obtains significant and large-span feature combinations. To learn the preference dynamics, a novel temporal action embedding represents user actions by incorporating the embeddings of items and temporal context as the inputs of the convolutional network. The experiments on typical public data sets demonstrate that TARN outperforms state-of-the-art methods and show the necessity and contribution of involving time-aware preference dynamics and explicit user/item feature couplings in modeling and interpreting evolving user preferences.
在推荐系统中，无论是静态的还是动态的用户对物品的偏好，都嵌入在用户与物品的交互（例如，评分或点击）及其上下文之中。顺序推荐系统（SRS）需要根据用户和物品特征之间的耦合以及用户对物品的连续动作，共同涉及到这种上下文感知的用户-物品交互。然而，这种联合建模并不简单，对现有的偏好建模工作提出了很大的挑战，因为这些工作要么只通过潜在因素模型来建模用户-物品交互，而忽略了用户偏好的动态变化，要么只捕捉到了顺序用户行为模式，而没有涉及用户/物品特征以及它们对用户行为的耦合和影响。我们提出了一种带有时间上下文的神经时间感知推荐网络（TARN），用于联合建模：1）通过特征交互网络来表示静态用户偏好；2）通过定制的卷积网络来表示用户偏好的动态变化。特征交互网络通过其特征嵌入的内积，对用户、物品和时间上下文的非零特征之间的成对耦合进行因式分解，同时缓解了数据稀疏性问题。在卷积网络中，我们引入了一个具有多个滤波器宽度的卷积层，以捕获多倍的顺序模式，其中注意平均池化（AAP）获得显著的、跨度较大的特征组合。为了学习偏好动态变化，我们使用一种新颖的时间动作嵌入，将物品和时间上下文的嵌入作为卷积网络的输入来表示用户动作。在典型的公共数据集上的实验表明，TARN 优于最先进的方法，并展示了在建模和解释不断变化的用户偏好时，考虑时间感知的偏好动态变化和显式的用户/物品特征耦合的必要性和贡献。
