ARCHIVES
Research Article
Hierarchical Alignment with Polar Contrastive Learning for Next-Basket Recommendation
C. Sathish1
Vinnarasu D2
Arun Kumar S3
Gowtham M4
Santhosh V5
1Assistant Professor, Department of Information Technology, Er. Perumal Manimekalai College of Engineering, Hosur, Tamil Nadu, India. 2345 Department of Information Technology, Er. Perumal Manimekalai College of Engineering, Hosur, Tamil Nadu, India.
Published Online: May-August 2024
Pages: 127-130
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20240302018
References
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IEEE Trans. Knowl. Data Eng., vol. 34, no. 7, pp. 3029–3043, Jul. 2022.
3. D. Le, H. W. Lauw, and Y. Fang, “Correlation-sensitive next-basket recommendation,” in Proc. 28th Int. Joint Conf. Artif. Intell., 2019, pp.
2808–2814.
4. Y. Qin, P. Wang, and C. Li, “The world is binary: Contrastive learning for denoising next basket recommendation,” in Proc. 44th Int. ACM
SIGIR Conf. Res. Develop. Inf. Retrieval, 2021, pp. 859–868.
5. X. Wang, D. Wang, C. Xu, X. He, Y. Cao, and T. Chua, “Explainable reasoning over knowledge graphs for recommendation,” in Proc. 33rd
AAAI Conf. Artif. Intell., 31st Innov. Appl. Artif. Intell. Conf., 2019, pp. 5329–5336.
6. F. Yu, Q. Liu, S. Wu, L. Wang, and T. Tan, “A dynamic recurrent model for next basket recommendation,” in Proc. 39th Int. ACM SIGIR
Conf. Res. Develop. Inf. Retrieval, 2016, pp. 729–732.
7. X. Fan, Z. Liu, J. Lian, W. X. Zhao, X. Xie, and J. Wen, “Lighter and better: Low-rank decomposed self-attention networks for next-item
recommendation,” in Proc. 44th Int. ACM SIGIR Conf. Res. Develop. Inf. Retrieval, 2021, pp. 1733–1737.
8. M. M. Tanjim, C. Su, E. Benjamin, D. Hu, L. Hong, and J. J. McAuley, “At- tentive sequential models of latent intent for next item
recommendation,” in Proc. ACM Web Conf., 2020, pp. 2528–2534.
9. Q. Cui, S. Wu, Q. Liu, W. Zhong, and L. Wang, “MV-RNN: A multi-view recurrent neural network for sequential recommendation,” IEEE
Trans. Knowl. Data Eng., vol. 32, no. 2, pp. 317–331, Feb. 2020.
10. K. Zhou et al., “S3-rec: Self-supervised learning for sequential recom- mendation with mutual information maximization,” in Proc. 29th
ACM Int. Conf. Inf. Knowl. Manage., 2020, pp. 1893–1902.
11. X. Zheng, M. Wang, R. Xu, J. Li, and Y. Wang, “Modeling dynamic missingness of implicit feedback for sequential recommendation,” IEEE
Trans. Knowl. Data Eng., vol. 34, no. 1, pp. 405–418, Jan. 2022.
12. Y. Chen, Z. Liu, J. Li, J. J. McAuley, and C. Xiong, “Intent contrastive learning for sequential recommendation,” in Proc. ACM Web Conf.,
2022, pp. 2172–2182.
2019, pp. 3940–3946.
2. C. Wang, W. Xi, L. Huang, Y. Zheng, Z. Hu, and J. Lai, “A BP neural network based recommender framework with attention mechanism,”
IEEE Trans. Knowl. Data Eng., vol. 34, no. 7, pp. 3029–3043, Jul. 2022.
3. D. Le, H. W. Lauw, and Y. Fang, “Correlation-sensitive next-basket recommendation,” in Proc. 28th Int. Joint Conf. Artif. Intell., 2019, pp.
2808–2814.
4. Y. Qin, P. Wang, and C. Li, “The world is binary: Contrastive learning for denoising next basket recommendation,” in Proc. 44th Int. ACM
SIGIR Conf. Res. Develop. Inf. Retrieval, 2021, pp. 859–868.
5. X. Wang, D. Wang, C. Xu, X. He, Y. Cao, and T. Chua, “Explainable reasoning over knowledge graphs for recommendation,” in Proc. 33rd
AAAI Conf. Artif. Intell., 31st Innov. Appl. Artif. Intell. Conf., 2019, pp. 5329–5336.
6. F. Yu, Q. Liu, S. Wu, L. Wang, and T. Tan, “A dynamic recurrent model for next basket recommendation,” in Proc. 39th Int. ACM SIGIR
Conf. Res. Develop. Inf. Retrieval, 2016, pp. 729–732.
7. X. Fan, Z. Liu, J. Lian, W. X. Zhao, X. Xie, and J. Wen, “Lighter and better: Low-rank decomposed self-attention networks for next-item
recommendation,” in Proc. 44th Int. ACM SIGIR Conf. Res. Develop. Inf. Retrieval, 2021, pp. 1733–1737.
8. M. M. Tanjim, C. Su, E. Benjamin, D. Hu, L. Hong, and J. J. McAuley, “At- tentive sequential models of latent intent for next item
recommendation,” in Proc. ACM Web Conf., 2020, pp. 2528–2534.
9. Q. Cui, S. Wu, Q. Liu, W. Zhong, and L. Wang, “MV-RNN: A multi-view recurrent neural network for sequential recommendation,” IEEE
Trans. Knowl. Data Eng., vol. 32, no. 2, pp. 317–331, Feb. 2020.
10. K. Zhou et al., “S3-rec: Self-supervised learning for sequential recom- mendation with mutual information maximization,” in Proc. 29th
ACM Int. Conf. Inf. Knowl. Manage., 2020, pp. 1893–1902.
11. X. Zheng, M. Wang, R. Xu, J. Li, and Y. Wang, “Modeling dynamic missingness of implicit feedback for sequential recommendation,” IEEE
Trans. Knowl. Data Eng., vol. 34, no. 1, pp. 405–418, Jan. 2022.
12. Y. Chen, Z. Liu, J. Li, J. J. McAuley, and C. Xiong, “Intent contrastive learning for sequential recommendation,” in Proc. ACM Web Conf.,
2022, pp. 2172–2182.
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