Multilevel collaborative top-n recommendation based on enhanced behavior

被引：0
|
作者：
Liu Y. [1 ]
Lyu Y. [1 ]
机构：
[1] School of Computer Science and Technology, Harbin University of Science and Technology, Harbin
来源：
Harbin Gongcheng Daxue Xuebao/Journal of Harbin Engineering University | 2024年 / 45卷 / 06期
关键词：
auxiliary behavior; graph neural network; high-order heterogenous signal; metapath graph; multibehavior; propagation layer; target behavior; user-item;
D O I：
10.11990/jheu.202204020
中图分类号：
学科分类号：
摘要：
Traditional recommendation systems only use one type of user behavior. However, multiple behaviors of users are related; therefore, ignoring user behaviors will result in the loss of the influence of auxiliary behavior on the target behavior. This paper proposes a multilevel collaborative Top-N recommendation based on enhanced user behavior (MCREB), which uses the attention mechanism to propagate information on the recommendation bipartite and item-based metapath graphs and learns multilevel high-order and heterogeneous collaborative signals, including user-item and inter-item, to improve recommendation performance. Thus, the model can better use the recommendation graph structure and fully consider the interaction between various behaviors on the recommendation graph structure. Furthermore, comprehensive experiments are conducted on the benchmark dataset to verify the model′s effectiveness. © 2024 Editorial Board of Journal of Harbin Engineering. All rights reserved.
引用
收藏
页码：1119 / 1126
页数：7
相关论文
共 26 条
  • [1] XU Pengyu, Liu Huafeng, Liu Bing, Et al., Survey of tag recommendation methods, Journal of Software, 33, 4, pp. 1244-1266, (2022)
  • [2] ZHANG Yujie, DONG Zheng, MENG Xiangwu, Research on personalized advertising recommendation systems and their applications, Chinese journal of computers, 44, 3, pp. 531-563, (2021)
  • [3] SINGH A P, GORDON G J., Relational learning via collective matrix factorization, Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 650-658, (2008)
  • [4] ZHAO Zhe, CHENG Zhiyuan, HONG Lichan, Et al., Improving user topic interest profiles by behavior factorization, Proceedings of the 24th International Conference on World Wide Web, pp. 1406-1416, (2015)
  • [5] GUO Guibing, QIU Huihuai, TAN Zhenhua, Et al., Resolving data sparsity by multi-type auxiliary implicit feedback for recommender systems, Knowledge-based systems, 138, pp. 202-207, (2017)
  • [6] GAO Chen, HE Xiangnan, GAN Dahua, Et al., Neural multi-task recommendation from multi-behavior data, 2019 IEEE 35th International Conference on Data Engineering (ICDE), pp. 1554-1557, (2019)
  • [7] XIA Qiaolin, JIANG Peng, SUN Fei, Et al., Modeling consumer buying decision for recommendation based on multitask deep learning, Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp. 1703-1706, (2018)
  • [8] Chen GAO, HE Xiangnan, GAN Dahua, Et al., Learning to recommend with multiple cascading behaviors [J], IEEE transactions on knowledge and data engineering, 33, 6, pp. 2588-2601, (2021)
  • [9] CHEN Chong, ZHANG Min, ZHANG Yongfeng, Et al., Efficient heterogeneous collaborative filtering without negative sampling for recommendation, Proceedings of the AAAI conference on artificial intelligence, 34, 1, pp. 19-26, (2020)
  • [10] XIA Lianghao, HUANG Chao, XU Yong, Et al., Knowledge-enhanced hierarchical graph transformer network for multi-behavior recommendation, Proceedings of the AAAI conference on artificial intelligence, 35, 5, pp. 4486-4493, (2021)
123