基于复杂结构信息的图神经网络序列推荐算法

doi:10.19678/j.issn.1000-3428.0061308

计算机工程 ›› 2022, Vol. 48 ›› Issue (5): 82-90,97. doi: 10.19678/j.issn.1000-3428.0061308

基于复杂结构信息的图神经网络序列推荐算法

胡承佐^1,2, 王庆梅^1,2, 李迪超³, 王铮⁴

1. 北京科技大学国家材料服役安全科学中心, 北京 100083;
2. 南方海洋科学与工程广东省实验室, 广东珠海 519080;
3. 澳门大学计算机与信息科学系, 澳门 999078;
4. 北京科技大学计算机与通信工程学院, 北京 100083

收稿日期:2021-03-29 修回日期:2021-05-22 发布日期:2021-06-01
作者简介:胡承佐(1996—),男,硕士研究生,主研方向为推荐系统;王庆梅(通信作者),副研究员;李迪超,博士研究生;王铮,助理教授。
基金资助:
南方海洋科学与工程广东省实验室（珠海）创新团队建设项目（311020012）。

Sequence Recommendation Algorithm of Graph Neural Networks Based on Complex Structure Information

HU Chengzuo^1,2, WANG Qingmei^1,2, LI Dichao³, WANG Zheng⁴

1. National Center for Materials Service Safety, University of Science and Technology Beijing, Beijing 100083, China;
2. Southern Marine Science and Engineering Guangdong Laboratory, Zhuhai, Guangdong 519080, China;
3. Department of Computer and Information Science, University of Macau, Macao 999078, China;
4. School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China

Received:2021-03-29 Revised:2021-05-22 Published:2021-06-01

摘要/Abstract

摘要： 图结构因其在序列推荐场景中的自然适应性而备受关注，而现有的基于图神经网络的会话序列推荐算法虽然能够利用图结构信息达到较好的推荐效果，但是没有考虑用户在会话序列中的重复点击行为和项目之间的复杂转换，且未很好地利用图中复杂的结构信息，导致推荐的效果受到一定程度的限制。提出有向与无向信息同注意力相融合的图神经网络序列推荐算法，并基于推荐算法给出项目隐含向量建模算法，结合会话序列图中的有向结构信息与无向结构信息，通过考虑用户的重复点击行为和引入注意力机制建立会话中点击项目的复杂转换模型。图节点在特征传播的过程中平衡邻居节点信息与自身信息的比例，以更准确地预测推荐过程中生成的会话向量。在Diginetica、Yoochoose 1/64、Yoochoose 1/4 3个数据集上的实验结果表明，与SR-GNN、TAGNN算法相比，该算法精度最高提升4.34%，能够更好地预测用户在会话中的下一次点击精度。

关键词: 图结构, 图神经网络, 会话序列, 推荐算法, 注意力机制

Abstract: Graph structures have received significant attention, owing to their natural adaptability for sessions.Thus, many researchers have investigated Graph Neural Networks (GNN)-based recommending algorithms and achieved state-of-the-art performances.Existing session-based recommendations based on GNN can yield relatively accurate recommendations, utilizing structural graph information.However, they neither consider repetitive submissions from users and complex transition between items nor fully utilize complex graph structural information.Consequently, they result in prediction losses.This paper proposes a GNN sequence recommendation algorithm based on the fusion of directed and undirected information with attention.The proposed algorithm combines directed and undirected structural information of session graphs into new hidden embeddings of items.By using repetitive behavioral information and attention mechanisms, the model incorporates complex transitions of items to form better session embeddings.During feature propagation, each node strikes a balance between preserving its information and absorbing its neighbors' information, improving the accuracy of recommendation predictions.The experimental results for Diginetica, Yoochoose 1/64, and Yoochoose 1/4 data sets show that compared with the best existing algorithms, that is, Session-based Recommendation with GNN (SR-GNN) and Target Attentive GNN (TAGNN), the accuracy of the algorithm can be improved by up to 4.34%. The proposed algorithm can predict the accuracy of the user's next click better in a session.

Key words: graph structure, Graph Neural Networks(GNN), session sequence, recommending algorithm, attention mechanism

中图分类号:

TP399

胡承佐, 王庆梅, 李迪超, 王铮. 基于复杂结构信息的图神经网络序列推荐算法[J]. 计算机工程, 2022, 48(5): 82-90,97.

HU Chengzuo, WANG Qingmei, LI Dichao, WANG Zheng. Sequence Recommendation Algorithm of Graph Neural Networks Based on Complex Structure Information[J]. Computer Engineering, 2022, 48(5): 82-90,97.

https://www.ecice06.com/CN/Y2022/V48/I5/82

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