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.
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页码：1119 / 1126
页数：7
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