基于知识增强对比学习的长尾用户序列推荐算法
Sequential recommendation algorithm for long-tail users based on knowledge-enhanced contrastive learning
摘要
Abstract
Sequential recommendation predicts next items for users based on their historical interactions.Existing meth-ods capture long-term dependencies but struggle to recommend precisely for users with short interaction sequences,espe-cially for long-tail users.Therefore,a sequential recommendation algorithm for long-tail users based on knowledge-enhanced contrastive learning was proposed.Firstly,semantic item similarity was introduced by leveraging relationships between entities in the knowledge graph to extract correlated items from original sequences.Secondly,two sequence aug-mentation operators were proposed based on different contrastive learning views,addressing the problem of insufficient training for long-tail user sequences by augmenting self-supervised signals.Finally,precise sequence recommendations were provided for long-tail users by utilizing the joint training of shared network parameters between contrastive self-supervised tasks and the recommendation task.Experimental results on real-world datasets demonstrate the effectiveness of the proposed algorithm in improving performance for long-tail users.关键词
序列推荐/长尾用户/知识图谱/对比学习Key words
sequential recommendation/long-tail user/knowledge graph/contrastive learning分类
信息技术与安全科学引用本文复制引用
任永功,周平磊,张志鹏..基于知识增强对比学习的长尾用户序列推荐算法[J].通信学报,2024,45(6):210-222,13.基金项目
国家自然科学基金资助项目(No.61976109) （No.61976109）
辽宁省"兴辽英才计划"基金资助项目(No.XLYC2006005) （No.XLYC2006005）
辽宁省高等学校科学研究基金资助项目(No.LJKZ0963) （No.LJKZ0963）
辽宁省科技厅重点研发基金资助项目(No.2022JH2/101300271) （No.2022JH2/101300271）
辽宁省教育厅校基本科研基金资助项目(No.LJKQZ20222431) （No.LJKQZ20222431）
教育部产学合作协同育人基金资助项目(No.202102550005) （No.202102550005）
辽宁省属本科高校基本科研基金资助项目(No.LS2024Q007)The National Natural Science Foundation of China(No.61976109),Liaoning Revitalization Talents Program(No.XLYC2006005),The Scientific Research Project of Liaoning Province(No.LJKZ0963),Key Research and Development Proj-ects of Liaoning Provincial Department of Science and Technology(No.2022JH2/101300271),Liaoning Province Ministry of Educa-ion(No.LJKQZ20222431),Industry-University Collaborative Education Project of the Ministry of Education(No.202102550005),Basic Research Project of Liaoning Provincial Ondergraduate Universities(No.LS2024Q007) （No.LS2024Q007）
