计算机工程与应用2024，Vol.60Issue(11)：268-280,13.DOI:10.3778/j.issn.1002-8331.2304-0290
面向超图的可解释性对比元路径群组推荐
Hypergraph-Based Meta-Path Explanation Contrastive Learning for Group Recommendation
摘要
Abstract
The large and sparse data in group recommendation often tend to ignore the complex dependencies between user groups and items,so fusing different user preference behavioral embeddings to make the performance of user dependen-cies on groups more intuitive,and also for the purpose of enhancing the view effect in comparison to obtain more accurate recommendation results,an interpretable comparison meta-path group recommendation framework based on hypergraph is proposed.By aggregating dependencies between groups of user items,it constructs meta-paths to represent different types of interactions between entities to promote entity similarity and more accurately obtain users'in-group and out-group interactions from the data.By combining interpretable models with contrast learning techniques,it improves the interpretability and performance of the models.By interpreting guided enhancement operations on positive and negative views generated on the model framework combined with self-supervised contrast learning,it addresses the above issues.This experiment validates the effectiveness of the proposed approach by conducting experiments on real datasets.关键词
群组推荐/超图学习/元路径/推荐系统/对比学习Key words
group recommendation/hypergraph learning/meta-paths/recommendation system/contrastive learning分类
信息技术与安全科学引用本文复制引用
漆盛,高榕,邵雄凯,吴歆韵,万祥,高海燕..面向超图的可解释性对比元路径群组推荐[J].计算机工程与应用,2024,60(11):268-280,13.基金项目
国家自然科学基金(61902116) （61902116）
南京大学计算机软件新技术国家重点实验室开放课题(KFKT2021B12) （KFKT2021B12）
湖北省高层次人才基金(GCRC2020011) （GCRC2020011）
湖北工业大学博士科研启动基金(BSQD2019026,BSQD2019022) （BSQD2019026,BSQD2019022）
湖北省自然科学基金(2021CFB273) （2021CFB273）
教育部春晖计划合作科研项目(HZKY20220350). （HZKY20220350）
