Meta graph network recommendation based on multi-behavior encoding

作者全名：Liu, Xiaoyang; Xiao, Wei; Liu, Chao; Wang, Wei; Li, Chaorong

作者地址：[Liu, Xiaoyang; Xiao, Wei; Liu, Chao] Chongqing Univ Technol, Sch Comp Sci & Engn, Chongqing 400054, Peoples R China; [Wang, Wei] Chongqing Med Univ, Sch Publ Hlth, Chongqing 400016, Peoples R China; [Li, Chaorong] Yibin Univ, Sch Artificial Intelligence & Big Data, Yibin 644000, Sichuan, Peoples R China

通信作者：Liu, XY (通讯作者)，Chongqing Univ Technol, Sch Comp Sci & Engn, Chongqing 400054, Peoples R China.

来源：JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES

ESI学科分类：　

WOS号：WOS:001241480700001

JCR分区：Q1

影响因子：5.2

年份：2024

卷号：36

期号：5

开始页：　

结束页：　

文献类型：Article

关键词：Multi-behavior; Meta-learning; Graph neural network; Recommendation system

摘要：As traditional recommendation systems ignore the hidden information among different user behaviors (such as clicks, add -to -favorites, add -to -cart, and purchases), this often leads to low accuracy in recommendation results. We propose a meta -graph network recommendation system via multi -behavior encoding (MBGR). Firstly, the graph convolutional neural network is used to extract features from various interactive behavior heterogeneous graphs of user -items for behavior heterogeneous modeling. Secondly, matrix decomposition algorithm and metaknowledge learner are used respectively to process the semantic information of user behavior, and then attention mechanism is used to learn and distinguish the importance of different types of user item interaction behaviors. Finally, meta -knowledge transfer network is used to combine meta -learning paradigm and neural network framework to establish user target behavior recommendation. We conducted comparative experiments comparing MBGR with 7 different baseline models such as NCF and DMF. Extensive experiments on three real datasets (Tmall, Yelp, ML10M) demonstrate that the proposed MBGR method outperforms the baselines. The performance of MBGR is improved by 10.97 % on average with the metric of HR@10 and 10.96 % with the metric of NDCG@10. Under different top -N value evaluation conditions (HR@10, HR@7, NDCG@10, NDCG@7, etc.), the proposed model ' s performance can also be improved by more than 10 %, which proves the rationality and effectiveness of the proposed MBGR method.

基金机构：Chongqing Federation of Social Science Key Project [2023NDZD09]; Graduate Innovation Fund of Chongqing University of Technology [gzlcx20233224]

基金资助正文：<BOLD>Acknowledgements</BOLD> This work is supported in part by Chongqing Federation of Social Science Key Project (2023NDZD09) ; Graduate Innovation Fund of Chongqing University of Technology (gzlcx20233224) .