Abstract Traditional recommender systems help users find items of interest by modeling long-term user profiles, which consist of items the users interacted with in the past. In many real-life applications, however, informative user profiles are not available. Instead, the system recommends by relying on the current user activities within an ongoing session, leading to the emergence of session-based recommendation methods. Among techniques used in such situations, recurrent neural networks (RNNs) present a natural choice thanks to their ability to model the order of session events and capture long-term dependencies. Recently, methods based on convolutional neural networks (CNNs) have also shown their potential in modeling session data, especially in extracting complex local patterns that are predictive of the user target. In this work, we propose a recurrent convolutional architecture that takes the advantages of both complex local features extracted by CNNs and long-term dependencies learned by RNNs from session sequences. Our model has two main layers: the lower layer consists of convolutional filters applied over consecutive session event embeddings, and the upper layer is a gated recurrent unit (GRU) RNN that takes as input the CNN’s output. This hybrid approach provides a flexible and unified network architecture for modeling various important features of session sequences. Experiments conducted on three benchmark datasets demonstrate the superiority of the proposed model over pure RNNs and CNNs models as well as state-of-the-art session-based recommendation methods.

中文翻译：

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用于基于会话的推荐的循环卷积网络

摘要 传统的推荐系统通过对长期用户档案进行建模来帮助用户找到感兴趣的物品，这些档案由用户过去与之交互的物品组成。然而，在许多现实生活应用程序中，信息丰富的用户配置文件是不可用的。相反，系统依靠正在进行的会话中的当前用户活动来进行推荐，从而导致了基于会话的推荐方法的出现。在这种情况下使用的技术中，循环神经网络 (RNN) 是一种自然选择，因为它们能够对会话事件的顺序进行建模并捕获长期依赖关系。最近，基于卷积神经网络 (CNN) 的方法也显示了它们在建模会话数据方面的潜力，尤其是在提取可预测用户目标的复杂局部模式方面。在这项工作中，我们提出了一种循环卷积架构，它利用了 CNN 提取的复杂局部特征和 RNN 从会话序列中学习到的长期依赖关系。我们的模型有两个主要层：下层由应用于连续会话事件嵌入的卷积滤波器组成，上层是门控循环单元 (GRU) RNN，它将 CNN 的输出作为输入。这种混合方法为对会话序列的各种重要特征进行建模提供了灵活且统一的网络架构。在三个基准数据集上进行的实验证明了所提出的模型优于纯 RNN 和 CNN 模型以及最先进的基于会话的推荐方法。