Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (9): 2704-2710.DOI: 10.11772/j.issn.1001-9081.2023091264

• Data science and technology • Previous Articles     Next Articles

Recommendation model combining self-features and contrastive learning

Xingyao YANG1(), Yu CHEN1, Jiong YU1, Zulian ZHANG2, Jiaying CHEN1, Dongxiao WANG1   

  1. 1.School of Software,Xinjiang University,Urumqi Xinjiang 830091,China
    2.Xinjiang Xinnong Network Information Center,Meteorological Bureau of Xinjiang Uygur Autonomous Region,Urumqi Xinjiang 830002,China
  • Received:2023-09-14 Revised:2023-10-26 Accepted:2023-10-31 Online:2023-11-23 Published:2024-09-10
  • Contact: Xingyao YANG
  • About author:CHEN Yu, born in 2000, M. S. candidate. His research interests include recommender system.
    YU Jiong, born in 1964, Ph. D., professor. His research interests include grid computing, parallel computing.
    ZHANG Zulian, born in 1984, M. S., senior engineer. Her research interests include numerical prediction, information retrieval.
    CHEN Jiaying, born in 1988, Ph. D., associate professor. Her research interests include recommender system, social network, data mining.
    WANG Dongxiao, born in 1970, experimentalist. His research interests include cyber security.
  • Supported by:
    Natural Science Foundation of Xinjiang Uygur Autonomous Region(2023D01C17);National Natural Science Foundation of China(62262064);Xinjiang Meteorological Bureau Guidance Project(YD202212);Xinjiang Uygur Autonomous Region Science and Technology Program(2023D4012)

结合自我特征和对比学习的推荐模型

杨兴耀1(), 陈羽1, 于炯1, 张祖莲2, 陈嘉颖1, 王东晓1   

  1. 1.新疆大学 软件学院,乌鲁木齐 830091
    2.新疆维吾尔自治区气象局 新疆兴农网信息中心,乌鲁木齐 830002
  • 通讯作者: 杨兴耀
  • 作者简介:杨兴耀(1984—),男,湖北襄阳人,副教授,博士,CCF会员,主要研究方向:推荐系统、大数据、信任计算
    陈羽(2000—),男,湖南岳阳人,硕士研究生,主要研究方向:推荐系统
    于炯(1964—),男,新疆乌鲁木齐人,教授,博士,CCF会员,主要研究方向:网格计算、并行计算
    张祖莲(1984—),女,湖北襄阳人,高级工程师,硕士,主要研究方向:数值预报、信息检索
    陈嘉颖(1988—),女,新疆沙湾人,副教授,博士,CCF会员,主要研究方向:推荐系统、社交网络、数据挖掘
    王东晓(1970—),男,黑龙江绥化人,实验师,主要研究方向:网络安全。
  • 基金资助:
    新疆维吾尔自治区自然科学基金资助项目(2023D01C17);国家自然科学基金资助项目(62262064);新疆气象局引导项目(YD202212);新疆维吾尔自治区科技计划项目(2023D4012)

Abstract:

Aiming at the over-smoothing and noise problems in the embedding representation in the message passing process of graph convolution based on graph neural network recommendation, a Recommendation model combining Self-features and Contrastive Learning (SfCLRec) was proposed. The model was trained using a pre-training-formal training architecture. Firstly, the embedding representations of users and items were pre-trained to maintain the feature uniqueness of the nodes themselves by fusing the node self-features and a hierarchical contrastive learning task was introduced to mitigate the noisy information from the higher-order neighboring nodes. Then, the collaborative graph adjacency matrix was reconstructed according to the scoring mechanism in the formal training stage. Finally, the predicted score was obtained based on the final embedding. Compared with existing graph neural network recommendation models such as LightGCN and Simple Graph Contrastive Learning (SimGCL), SfCLRec achieves the better recall and NDCG (Normalized Discounted Cumulative Gain) in three public datasets ML-latest-small, Last.FM and Yelp, validating the effectiveness of SfCLRec.

Key words: graph collaborative filtering, over-smoothing, self-feature, contrastive learning, graph neural network, personalized recommendation

摘要:

针对图神经网络推荐中图卷积在消息传递过程的嵌入表示过平滑和噪声问题,提出一种结合自我特征和对比学习的推荐模型(SfCLRec)。采用预训练-正式训练架构训练模型,首先预训练用户和项目的嵌入表示,通过融合节点自我特征维持节点本身的特征唯一性,并引入层级对比学习任务减少来自高阶邻居节点中的噪声;其次,在正式训练阶段根据评分机制重新构建协同图邻接矩阵;最后,根据最终嵌入得到预测评分。实验结果表明,相较于LightGCN、SimGCL(Simple Graph Contrastive Learning)等现有图神经网络推荐模型,SfCLRec在3个公开数据集ML-latest-small、Last.FM和Yelp中均取得了较好的召回率和归一化折损累计增益(NDCG),验证了SfCLRec的有效性。

关键词: 图协同过滤, 过平滑, 自我特征, 对比学习, 图神经网络, 个性化推荐

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