Hou Zhiwen, Bu Fanliang, Zhou Yuchen, Bu Lingbin, Ma Qiming, Wang Yifan, Zhai Hanming, Han Zhuxuan
School of Information Network Security, People's Public Security University of China, Beijing 100038, China.
Math Biosci Eng. 2024 Feb 5;21(3):3563-3593. doi: 10.3934/mbe.2024157.
Dynamic recommendation systems aim to achieve real-time updates and dynamic migration of user interests, primarily utilizing user-item interaction sequences with timestamps to capture the dynamic changes in user interests and item attributes. Recent research has mainly centered on two aspects. First, it involves modeling the dynamic interaction relationships between users and items using dynamic graphs. Second, it focuses on mining their long-term and short-term interaction patterns. This is achieved through the joint learning of static and dynamic embeddings for both users and items. Although most existing methods have achieved some success in modeling the historical interaction sequences between users and items, there is still room for improvement, particularly in terms of modeling the long-term dependency structures of dynamic interaction histories and extracting the most relevant delayed interaction patterns. To address this issue, we proposed a Dynamic Context-Aware Recommendation System for dynamic recommendation. Specifically, our model is built on a dynamic graph and utilizes the static embeddings of recent user-item interactions as dynamic context. Additionally, we constructed a Gated Multi-Layer Perceptron encoder to capture the long-term dependency structure in the dynamic interaction history and extract high-level features. Then, we introduced an Attention Pooling network to learn similarity scores between high-level features in the user-item dynamic interaction history. By calculating bidirectional attention weights, we extracted the most relevant delayed interaction patterns from the historical sequence to predict the dynamic embeddings of users and items. Additionally, we proposed a loss function called the Pairwise Cosine Similarity loss for dynamic recommendation to jointly optimize the static and dynamic embeddings of two types of nodes. Finally, extensive experiments on two real-world datasets, LastFM, and the Global Terrorism Database showed that our model achieves consistent improvements over state-of-the-art baselines.
动态推荐系统旨在实现用户兴趣的实时更新和动态迁移，主要利用带有时间戳的用户-物品交互序列来捕捉用户兴趣和物品属性的动态变化。近期的研究主要集中在两个方面。首先，它涉及使用动态图对用户和物品之间的动态交互关系进行建模。其次，它专注于挖掘它们的长期和短期交互模式。这是通过对用户和物品的静态及动态嵌入进行联合学习来实现的。尽管大多数现有方法在对用户和物品之间的历史交互序列进行建模方面取得了一些成功，但仍有改进的空间，特别是在对动态交互历史的长期依赖结构进行建模以及提取最相关的延迟交互模式方面。为了解决这个问题，我们提出了一种用于动态推荐的动态上下文感知推荐系统。具体而言，我们的模型基于动态图构建，并利用近期用户-物品交互的静态嵌入作为动态上下文。此外，我们构建了一个门控多层感知器编码器来捕捉动态交互历史中的长期依赖结构并提取高级特征。然后，我们引入了一个注意力池化网络来学习用户-物品动态交互历史中高级特征之间的相似度分数。通过计算双向注意力权重，我们从历史序列中提取最相关的延迟交互模式以预测用户和物品的动态嵌入。此外，我们提出了一种用于动态推荐的成对余弦相似度损失函数，以联合优化两种类型节点的静态和动态嵌入。最后，在两个真实世界数据集LastFM和全球恐怖主义数据库上进行的大量实验表明，我们的模型相对于最先进的基线实现了持续的改进。
