| 36 | 0 | 25 |
| Downloads | Citas | Reads |
Sequence recommendation aims at item recommendation using users' long and short-term preferences, but most sequence recommendation systems face problems such as insufficient learning power and inadequate fusion of long and shortterm preferences. Aiming at the above problems, this paper proposes a fine-grained long and short-term preference sequence recommendation method based on contrastive learning. 1) To address the problem of insufficient long and short-term preference fusion, this paper proposes a long and short-term preference learning layer and a long and short-term preference fusion layer. Firstly, it splits the user behaviour sequence into multi-period sessions and extracts the user's short-term preference in each session by using gated recurrent units, and then fuses the short-term preference sequences to capture the user's long-term preference through the multi-head attention mechanism. Finally, the long-term and short-term preferences are fused adaptively based on the time span to obtain a more representative and comprehensive preference representation. 2) Aiming at the problem of insufficient learning power due to data sparsity, a preference representation comparison learning task is designed to introduce agent user preferences for comparison learning to achieve more accurate preference recommendation. The experimental results show that: compared to the sub-optimal methods, the model improves the Hit@20 metric by 9.84%, 6.40%, and 1.52%, and the MAP@20 metric by 22.64%, 2.42%, and 6.42% on three public datasets, respectively, demonstrating the effectiveness of the proposed method.
[1]Gao S,Fang J B,Tu Q,et al.Generative news recommendation[C]//Proceedings of the ACM Web Conference 2024,May 13-17,2024,Singapore,Singapore.New York:ACM,2024:3444-3453.
[2]Saxborn M,Pan Y C,Said A.Trust through recommendation in E-commerce[C]//Proceedings of the 2024 Conference on Human Information Interaction and Retrieval,March 10-14,2024,Sheffield,United Kingdom.New York:ACM,2024:87-96.
[3]Quan Y H,Ding J T,Gao C,et al.Alleviating video-length effect for micro-video recommendation[J].ACM Transactions on Information Systems,2023,42(2):44.
[4]Tang J X,Wang K.Personalized top-N sequential recommendation via convolutional sequence embedding[C]//Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining,February 5-9,2018,Marina Del Rey,CA,USA.New York:ACM,2018:565-573.
[5]Yuan F J,Karatzoglou A,Arapakis I,et al.A simple convolutional generative network for next item recommendation[C]//Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining,February 11-15,2019,Melbourne,VIC,Australia.New York:ACM,2019:582-590.
[6]Yan A,Cheng S,Kang W C,et al.CosRec:2D convolutional neural networks for sequential recommendation[C]//Proceedings of the 28th ACM International Conference on Information and Knowledge Management,November 3-7,2019,Beijing,China.New York:ACM,2019:2173-2176.
[7]Zheng L,Fan Z W,Lu C T,et al.Gated spectral units:Modeling co-evolving patterns for sequential recommendation[C]//Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval,July 21-25,2019,Paris,France.New York:ACM,2019:1077-1080.
[8]Sun F,Liu J,Wu J,et al.BERT4Rec:Sequential recommendation with bidirectional encoder representations from Tranformer[C]//Proceedings of the 28th ACM International Conference on Information and Knowledge Management,November 3-7,2019,Beijing,China.New York:ACM,2019:1441-1450.
[9]Li J C,Wang Y J,McAuley J,et al.Time interval aware self-attention for sequential recommendation[C]//Proceedings of the13th International Conference on Web Search and Data Mining,February 3-7,2020,Houston,TX,USA. New York:ACM,2020:322-330.
[10]Feng Y F,Lyu F Y,Shen W C,et al.Deep session interest network for click-through rate prediction[C]//Proceedings of the28th International Joint Conference on Artificial Intelligence,August 10-16,2019,Macao,China. Palo Alto:AAAI Press,2019:2301-2307.
[11]Xie R B,Wang Y L,Wang R,et al.Long short-term temporal meta-learning in online recommendation[C]//Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining,February 21-25,2022,Virtual Event,AZ,USA.New York:ACM,2022:1168-1176.
[12]An M X,Wu F Z,Wu C H,et al.Neural news recommendation with long-and short-term user representations[C]//Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics,July 28-August 2,2019,Florence,Italy.Cambridge:ACL,2019:336-345.
[13]Zheng Y,Gao C,Chang J X,et al.Disentangling long and short-term interests for recommendation[C]//Proceedings of the ACM Web Conference 2022,April 25-29,2022,Virtual Event,Lyon,France.New York:ACM,2022:2256-2267.
[14]Arthur J K,Zhou C H,Osei-Kwakye J,et al.A heterogeneous couplings and persuasive user/item information model for Next Basket recommendation[J].Engineering Applications of Artificial Intelligence,2022,114:105132.
[15]Li J,Ren P J,Chen Z M,et al.Neural attentive session-based recommendation[C]//Proceedings of the 2017 ACM on Conference on Information and Knowledge Management,November 6-10,2017,Singapore,Singapore. New York:ACM,2017:1419-1428.
[16]Arthur J K, Zhou C H,Mantey E A,et al.A discriminative-based geometric deep learning model for cross domain recommender systems[J].Applied Sciences,2022,12:5202.
[17]Zhao W,Wang B Y,Ye J B,et al.PLASTIC:Prioritize long and short-term information in top-n recommendation using adversarial training[C]//Proceedings of the 27th International Joint Conference on Artificial Intelligence,July 13-19,2018,Stockholm,Sweden.Palo Alto:AAAI Press,2018:3676-3682.
[18]Yu Z P,Lian J X,Mahmoody A,et al.Adaptive user modeling with long and short-term preferences for personalized recommendation[C]//Proceedings of the 28th International Joint Conference on Artificial Intelligence,August 10-16,2019,Macao,China.Palo Alto:AAAI Press,2019:4213-4219.
[19]Niu L S,Peng Y,Liu Y M.Deep recommendation model combining long-and short-term interest preferences[J].IEEE Access,2021,9:166455-166464.
[20]He K,Liu Q Y,Li Q M,et al.Long-and short-term sequential recommendation with temporal interval[C]//2022 IEEE Smartworld,Ubiquitous Intelligence&Computing,Scalable Computing&Communications,Digital Twin,Privacy Computing,Metaverse,Autonomous&Trusted Vehicles,December 15-18,2022,Haikou,Hainan,China.New York:IEEE,2022:1409-1414.
[21]Yan C R,Wang Y W,Zhang Y T,et al.Modeling long-and short-term user behaviors for sequential recommendation with deep neural networks[C]//2021 International Joint Conference on Neural Networks(IJCNN),July 18-22,2021,Shenzhen,Guangdong,China.New York:IEEE,2021:1-8.
[22]Xie X,Sun F,Liu Z Y,et al.Contrastive learning for sequential recommendation[C]//2022 IEEE 38th International Conference on Data Engineering(ICDE),May 9-12,2022,Kuala Lumpur,Malaysia.New York:IEEE,2022:1259-1273.
[23]Yin P,Zhao J,Ma Z J,et al.Sequential recommendation based on multipair contrastive learning with informative augmentation[J].Neural Computing and Applications,2024,36:9707-9721.
[24]Ma J X,Zhou C,Yang H X,et al.Disentangled self-supervision in sequential recommenders[C]//Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery&Data Mining,August 23-27,2020,Virtual Event,CA,USA.New York:ACM,2020:483-491.
[25]Zhou K,Wang H,Zhao W X,et al.S3-Rec:Self-supervised learning for sequential recommendation with mutual information maximization[C]//Proceedings of the 29th ACM International Conference on Information&Knowledge Management,October19-23,2020,Virtual Event,Ireland.New York:ACM,2020:1893-1902.
[26]Hao Y J,Zhang T T,Zhao P P,et al.Feature-level deeper self-attention network with contrastive learning for sequential recommendation[J].IEEE Transactions on Knowledge and Data Engineering,2023,35(10):10112-10124.
[27]Bertin-Mahieux T,Ellis D P W,Whitman B,et al.The million song dataset[C/OL]//ISMIR,2011.https：//www.researchgate.net/publication/220723656_The_Million_Song_Dataset.
[28]Ruocco M,Skrede O S L,Langseth H.Inter-session modeling for session-based recommendation[C]//Proceedings of the 2nd Workshop on Deep Learning for Recommender Systems,August 27,2017,Como,Italy.New York:ACM,2017:24-31.
[29]Kang W C,McAuley J.Self-attentive sequential recommendation[C]//2018 IEEE International Conference on Data Mining(ICDM),November 17-20,2018,Singapore,Singapore.New York:IEEE,2018:197-206.
[30]Li J C,Wang Y J,McAuley J.Time interval aware self-attention for sequential recommendation[C]//Proceedings of the 13th International Conference on Web Search and Data Mining,February 3-7,2020,Houston,TX,USA.New York:ACM,2020:322-330.
[31]Zhang D H,Qin J W,Ma J,et al.Item attributes fusion based on contrastive learning for sequential recommendation[J].Multimedia Systems,2024,30:291.
[32]Ruan S Q,Yang C,Li D S.Knowledge-enhanced personalized hierarchical attention network for sequential recommendation[J].World Wide Web,2024,27:2.
[33]Li Y,Yang C C,Ni T G,et al.Long and short-term interest contrastive learning under filter-enhanced sequential recommendation[J].IEEE Access,2023,11:95928-95938.
Basic Information:
DOI：10.13568/j.cnki.651094.651316.2025.01.18.0001
China Classification Code:TP391.3;TP18
Citation Information:
[1]Yang Xingyao,Wu Yanfu,Zhang Zulian ,et al.Fine-Grained Long and Short-Term Preference Sequential Recommendation with Contrastive Learning[J].Journal of Xinjiang University (Natural Science Edition in Chinese and English),2026,43(02):156-168.DOI:10.13568/j.cnki.651094.651316.2025.01.18.0001.
Fund Information:
新疆维吾尔自治区自然科学基金面上项目“基于知识图谱与图神经网络的信息聚合及特征表示推荐技术研究”（2023D01C17）;“气温预报误差的地形依赖性与南疆高山区夏季高温智能网格预报技术研究”（2023D01A123）; 国家自然科学基金“大数据流式计算环境下基于预测的资源调度性能优化研究”（62262064）; 新疆维吾尔自治区科技计划项目-天山创新团队计划“面向农业的天地协同水资源时空精准调度研究及应用创新团队”（2023D4012）
2026-03-15
2026-03-15