软件导刊2024，Vol.23Issue(12)：27-35,9.DOI:10.11907/rjdk.232308
结合正负反馈状态表示的深度强化学习推荐方法
Deep Reinforcement Learning-Based Recommendation Method with Positive and Negative Feedback State Representation
摘要
Abstract
The application of deep reinforcement learning techniques in interactive recommendation systems has reached a high level of matu-rity.However,there is currently limited research dedicated to modeling there presentation of states.Existing works primarily focus on modeling state representations based on positive feedback sequences during user interactions.This approach results in the oversight of potential relation-ships existing within negative feedback sequences generated by users during interactions,as well as changes in user interests.Consequently,the recommendations produced by such systems tend to be one-sided.To address this gap,a novel recommendation system framework,named Contrastive Learning and Deep Reinforcement Learning-Based Recommender System(CRLRS),is proposed.CRLRS is designed to model state representations for both positive and negative feedback sequences generated during user interactions.Additionally,in order to mitigate data sparsity issues associated with positive feedback and address differences between fine-grained positive and negative feedback,a contras-tive auxiliary task is incorporated.Extensive experiments were conducted on two real-world datasets,among which HR@10 The results of the evaluation indicators on the Movielens-100k and Movielens-1m datasets are 0.705 2 and 0.490 2,respectively;NDCG@10 The results of the evaluation indicators are 0.478 2 and 0.271 5.The comparison results show that our method is significantly better than the current state-of-the-art methods,which proves the necessity of CRLRS modeling positive and negative feedback simultaneously and adding comparative auxil-iary tasks,and has better recommendation performance.关键词
深度强化学习/对比学习/推荐系统/正负反馈/状态表示Key words
deep reinforcement learning/contrastive learning/recommender system/positive negative feedback/state representation分类
信息技术与安全科学引用本文复制引用
张涛,张志军,曹家伟,范钰敏,刘佳慧,袁卫华..结合正负反馈状态表示的深度强化学习推荐方法[J].软件导刊,2024,23(12):27-35,9.基金项目
山东省自然科学基金项目(ZR2021MF099,ZR2022MF334) （ZR2021MF099,ZR2022MF334）
山东省教学改革研究项目(M2021130,M2022245,Z2022202) （M2021130,M2022245,Z2022202）
山东省优质专业学位教学案例库建设项目(SDYAL2022155) （SDYAL2022155）
山东省重点研发计划(软科学项目)(2021RKY03056) （软科学项目）
"海右计划"产业领军人才本土类创新团队项目(2023) （2023）
