Extrapolation-Based Data Augmentation for Sequential Recommendation
Resumo
With the advent of deep learning in the recommendation field, a lot ofwork has been and still is being done to bring deep learning-based models to their full potential. One line of work, Self-Supervised Learning (SSL), focuses on extracting the maximum potential of datasets to improve recommendation performance, and at the same time, attempts to diminish data-related problems, such as data sparsity, that are commonly seen in machine learning techniques. One branch of SSL for recommender systems uses predictive strategies to create new labels and examples for training by, for instance, adding new interactions to the user’s history of interactions. However, the existing models that explore this idea are somewhat limited. They focus on adding new interactions at the start of the sequences, ignoring the performance improvements that could be achieved by adding interactions in the middle and end of the sequences. We propose Extrapolation-based Sequence Augmentation for Sequential Recommendation (ESA4SRec), a model that uses the sequence reconstruction capabilities of BERT4Rec to generate new data at any position of a sequence by extrapolating the existing knowledge to unknown, novel interactions. The resulting augmented dataset is then used as input to a modelagnostic sequential recommender system. We compare our approach to related models and demonstrate the performance improvements when compared with the original datasets and the overall best performance of our method. ESA4SRec’s code available at https://github.com/viniciusgm000/ESA4SRec.
Palavras-chave:
Recommender Systems, Self-Supervised Learning, Sequential Recommendation, Data Augmentation
Referências
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Won-Seok Hwang, Shaoyu Li, Sang-Wook Kim, and Kichun Lee. 2018. Data imputation using a trust network for recommendation via matrix factorization. Computer Science and Information Systems 15, 2 (2018), 347–368.
Juyong Jiang, Peiyan Zhang, Yingtao Luo, Chaozhuo Li, Jae Boum Kim, Kai Zhang, Senzhang Wang, Sunghun Kim, and Philip S. Yu. 2025. Improving Sequential Recommendations via Bidirectional Temporal Data Augmentation With Pre-Training. IEEE Transactions on Knowledge and Data Engineering 37, 5 (2025), 2652–2664. DOI: 10.1109/TKDE.2025.3546035
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Jiacheng Li, Yujie Wang, and Julian McAuley. 2020. Time Interval Aware Self-Attention for Sequential Recommendation. In Proceedings of the 13th International Conference on Web Search and Data Mining (Houston, TX, USA) (WSDM ’20). Association for Computing Machinery, New York, NY, USA, 322–330. DOI: 10.1145/3336191.3371786
Lei Li, Yongfeng Zhang, and Li Chen. 2021. Personalized Transformer for Explainable Recommendation. arXiv:2105.11601 [cs.IR] [link]
Yujie Lin, Chenyang Wang, Zhumin Chen, Zhaochun Ren, Xin Xin, Qiang Yan, Maarten de Rijke, Xiuzhen Cheng, and Pengjie Ren. 2023. A Self-Correcting Sequential Recommender. In Proceedings of the ACMWeb Conference 2023 (Austin, TX, USA) (WWW ’23). Association for Computing Machinery, New York, NY, USA, 1283–1293. DOI: 10.1145/3543507.3583479
Qidong Liu, Jiaxi Hu, Yutian Xiao, Xiangyu Zhao, Jingtong Gao, Wanyu Wang, Qing Li, and Jiliang Tang. 2024. Multimodal Recommender Systems: A Survey. ACM Comput. Surv. 57, 2, Article 26 (Oct. 2024), 17 pages. DOI: 10.1145/3695461
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Zhiwei Liu, Ziwei Fan, Yu Wang, and Philip S. Yu. 2021. Augmenting Sequential Recommendation with Pseudo-Prior Items via Reversely Pre-training Transformer. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (Virtual Event, Canada) (SIGIR ’21). Association for Computing Machinery, New York, NY, USA, 1608–1612. DOI: 10.1145/3404835.3463036
Jianxin Ma, Chang Zhou, Hongxia Yang, Peng Cui, Xin Wang, and Wenwu Zhu. 2020. Disentangled Self-Supervision in Sequential Recommenders. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (Virtual Event, CA, USA) (KDD ’20). Association for Computing Machinery, New York, NY, USA, 483–491. DOI: 10.1145/3394486.3403091
Julian McAuley, Christopher Targett, Qinfeng Shi, and Anton van den Hengel. 2015. Image-Based Recommendations on Styles and Substitutes. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval (Santiago, Chile) (SIGIR ’15). Association for Computing Machinery, New York, NY, USA, 43–52. DOI: 10.1145/2766462.2767755
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Narjes Nikzad-Khasmakhi, Mohammad Ali Balafar, Mohammad Reza Feizi-Derakhshi, and Cina Motamed. 2021. BERTERS: Multimodal representation learning for expert recommendation system with transformers and graph embeddings. Chaos, Solitons & Fractals 151 (2021), 111260. DOI: 10.1016/j.chaos.2021.111260
Aleksandr Petrov and Craig Macdonald. 2022. A Systematic Review and Replicability Study of BERT4Rec for Sequential Recommendation. In Proceedings of the 16th ACM Conference on Recommender Systems (Seattle, WA, USA) (Rec-Sys ’22). Association for Computing Machinery, New York, NY, USA, 436–447. DOI: 10.1145/3523227.3548487
Ruihong Qiu, Zi Huang, Hongzhi Yin, and Zijian Wang. 2022. Contrastive Learning for Representation Degeneration Problem in Sequential Recommendation. In Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining (Virtual Event, AZ, USA) (WSDM ’22). Association for Computing Machinery, New York, NY, USA, 813–823. DOI: 10.1145/3488560.3498433
Massimo Quadrana, Paolo Cremonesi, and Dietmar Jannach. 2018. Sequence-Aware Recommender Systems. ACM Comput. Surv. 51, 4, Article 66 (July 2018), 36 pages. DOI: 10.1145/3190616
Shaina Raza and Chen Ding. 2019. Progress in context-aware recommender systems — An overview. Computer Science Review 31 (2019), 84–97. DOI: 10.1016/j.cosrev.2019.01.001
Steffen Rendle, Christoph Freudenthaler, and Lars Schmidt-Thieme. 2010. Factorizing personalized Markov chains for next-basket recommendation. In Proceedings of the 19th International Conference on World Wide Web (Raleigh, North Carolina, USA) (WWW ’10). Association for Computing Machinery, New York, NY, USA, 811–820. DOI: 10.1145/1772690.1772773
Kyuyong Shin, Hanock Kwak, Kyung-Min Kim, Minkyu Kim, Young-Jin Park, Jisu Jeong, and Seungjae Jung. 2021. One4all User Representation for Recommender Systems in E-commerce. arXiv:2106.00573 [cs.IR] [link]
Fei Sun, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou, and Peng Jiang. 2019. BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (Beijing, China) (CIKM ’19). Association for Computing Machinery, New York, NY, USA, 1441–1450. DOI: 10.1145/3357384.3357895
Jiaxi Tang and Ke Wang. 2018. Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding. In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining (Marina Del Rey, CA, USA) (WSDM ’18). Association for Computing Machinery, New York, NY, USA, 565–573. DOI: 10.1145/3159652.3159656
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Proceedings of the 31st International Conference on Neural Information Processing Systems (Long Beach, California, USA) (NIPS’17). Curran Associates Inc., Red Hook, NY, USA, 6000–6010.
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Publicado
10/11/2025
Como Citar
MACHADO, Vinicius Gabriel; SCHMITT, Murilo F. L.; SPINOSA, Eduardo J..
Extrapolation-Based Data Augmentation for Sequential Recommendation. In: BRAZILIAN SYMPOSIUM ON MULTIMEDIA AND THE WEB (WEBMEDIA), 31. , 2025, Rio de Janeiro/RJ.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 238-247.
DOI: https://doi.org/10.5753/webmedia.2025.15127.
