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Recommender systems are indispensable in the realm of online applications, and sequential recommendation has enjoyed considerable prevalence due to its capacity to encapsulate the dynamic shifts in user interests.
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th international conference on World Wide Web. pp. 285–295 (2001)
2001
Earlier work this paper cites.
2015
Earlier work this paper cites.
Cui, Q., Wu, S., Liu, Q., Zhong, W., Wang, L.: Mv-rnn: A multi-view recurrent neural network for sequential recommendation. IEEE Transactions on Knowledge and Data Engineering 32
2018
Earlier work this paper cites.
Kang, W.C., McAuley, J.: Self-attentive sequential recommendation. In: 2018 IEEE international conference on data mining (ICDM). pp. 197–206. IEEE (2018)
2018
Earlier work this paper cites.
Tang, J., Wang, K.: Personalized top-n sequential recommendation via convolutional sequence embedding. In: Proceedings of the eleventh ACM international conference on web search and data mining. pp. 565–573 (2018)
2018
Earlier work this paper cites.
Wan, M., McAuley, J.: Item recommendation on monotonic behavior chains. In: Proceedings of the 12th ACM conference on recommender systems. pp. 86–94 (2018)
2018
Earlier work this paper cites.
Sun, F., Liu, J., Wu, J., Pei, C., Lin, X., Ou, W., Jiang, P.: Bert4rec: Sequential recommendation with bidirectional encoder representations from transformer. In: Proceedings of the 28th ACM international conference on information and knowledge management. pp. 1441–1450 (2019)
2019
Earlier work this paper cites.
Zhang, T., Zhao, P., Liu, Y., Sheng, V.S., Xu, J., Wang, D., Liu, G., Zhou, X., et al.: Feature-level deeper self-attention network for sequential recommendation. In: IJCAI. pp. 4320–4326 (2019)
2019
Earlier work this paper cites.
Wu, F., Qiao, Y., Chen, J.H., Wu, C., Qi, T., Lian, J., Liu, D., Xie, X., Gao, J., Wu, W., et al.: Mind: A large-scale dataset for news recommendation. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. pp. 3597–3606 (2020)
2020
Earlier work this paper cites.
Zhou, K., Wang, H., Zhao, W.X., Zhu, Y., Wang, S., Zhang, F., Wang, Z., Wen, J.R.: S3-rec: Self-supervised learning for sequential recommendation with mutual information maximization. In: Proceedings of the 29th ACM international conference on information & knowledge management. pp. 1893–1902 (2020)
2020
Cited alongside, same era.
Li, X.L., Liang, P.: Prefix-tuning: Optimizing continuous prompts for generation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). pp. 4582–4597 (2021)
2021
Cited alongside, same era.
de Souza Pereira Moreira, G., Rabhi, S., Lee, J.M., Ak, R.e.a.: Transformers4rec: Bridging the gap between nlp and sequential/session-based recommendation. In: Proceedings of the 15th ACM Conference on Recommender Systems. pp. 143–153 (2021)
2021
Cited alongside, same era.
2022
Later among the works it cites.
Xie, X., Sun, F., Liu, Z., Wu, S., Gao, J., Zhang, J., Ding, B., Cui, B.: Contrastive learning for sequential recommendation. In: 2022 IEEE 38th international conference on data engineering (ICDE). pp. 1259–1273. IEEE (2022)
2022
Later among the works it cites.
Zhang, K., Liu, Q., Huang, Z., Cheng, M., Zhang, K., Zhang, M., Wu, W., Chen, E.: Graph adaptive semantic transfer for cross-domain sentiment classification. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. pp. 1566–1576 (2022)
2022
Later among the works it cites.
Li, L., Zhang, Y., Chen, L.: Personalized prompt learning for explainable recommendation. ACM Transactions on Information Systems 41
2023
Closest in time.
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2021
Cited alongside, same era.
Cheng, M., Liu, Z., Liu, Q., Ge, S., Chen, E.: Towards automatic discovering of deep hybrid network architecture for sequential recommendation. In: Proceedings of the ACM Web Conference 2022. pp. 1923–1932 (2022)
2022
Cited alongside, same era.
Geng, S., Liu, S., Fu, Z., Ge, Y.e.a.: Recommendation as language processing (rlp): A unified pretrain, personalized prompt & predict paradigm (p5). In: Proceedings of the 16th ACM Conference on Recommender Systems. pp. 299–315 (2022)
2022
Cited alongside, same era.
Liu, X., Ji, K., Fu, Y., Tam, W., Du, Z., Yang, Z., Tang, J.: P-tuning: Prompt tuning can be comparable to fine-tuning across scales and tasks. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). pp. 61–68 (2022)
2022
Cited alongside, same era.
Liu, Z., Cheng, M., Li, Z., Liu, Q., Chen, E.: One person, one model—learning compound router for sequential recommendation. In: 2022 IEEE International Conference on Data Mining (ICDM). pp. 289–298. IEEE (2022)
2022
Cited alongside, same era.
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners
Cited in the paper.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
Zhao, C., Zhao, H., Li, X., He, M., Wang, J., Fan, J.: Cross-domain recommendation via progressive structural alignment. IEEE Transactions on Knowledge and Data Engineering (2023)
2023
Closest in time.