He Qiang, Li Xinkai, Cai Biao
School of Mechanical and Electrical Engineering, Chengdu University of Technology, Chengdu, 610059, China.
School of Computer Science and Cyber Security, Chengdu University of Technology, Chengdu, 610059, China.
Sci Rep. 2024 Feb 15;14(1):3853. doi: 10.1038/s41598-024-54376-3.
In this era of information explosion, recommendation systems play a key role in helping users to uncover content of interest among massive amounts of information. Pursuing a breadth of recall while maintaining accuracy is a core challenge for current recommendation systems. In this paper, we propose a new recommendation algorithm model, the interactive higher-order dual tower (IHDT), which improves current models by adding interactivity and higher-order feature learning between the dual tower neural networks. A heterogeneous graph is constructed containing different types of nodes, such as users, items, and attributes, extracting richer feature representations through meta-paths. To achieve feature interaction, an interactive learning mechanism is introduced to inject relevant features between the user and project towers. Additionally, this method utilizes graph convolutional networks for higher-order feature learning, pooling the node embeddings of the twin towers to obtain enhanced end-user and item representations. IHDT was evaluated on the MovieLens dataset and outperformed multiple baseline methods. Ablation experiments verified the contribution of interactive learning and high-order GCN components.
在这个信息爆炸的时代，推荐系统在帮助用户从海量信息中发现感兴趣的内容方面发挥着关键作用。在保持准确性的同时追求召回率的广度是当前推荐系统面临的核心挑战。在本文中，我们提出了一种新的推荐算法模型——交互式高阶双塔（IHDT），它通过在双塔神经网络之间增加交互性和高阶特征学习来改进现有模型。构建了一个包含不同类型节点（如用户、物品和属性）的异构图，通过元路径提取更丰富的特征表示。为了实现特征交互，引入了一种交互式学习机制，在用户塔和项目塔之间注入相关特征。此外，该方法利用图卷积网络进行高阶特征学习，对双塔的节点嵌入进行池化，以获得增强的最终用户和物品表示。IHDT在MovieLens数据集上进行了评估，性能优于多种基线方法。消融实验验证了交互式学习和高阶GCN组件的贡献。
