Abstract
Collaborative filtering (CF) plays a key role in recommender systems, which consists of two basic disciplines: neighborhood methods and latent factor models. Neighborhood methods are most effective at capturing the very localized structure of a given rating matrix, while latent factor models are generally effective at capturing its global structure. However, two disciplines fail to capture these two structures simultaneously. Motivated by the sparse linear methods and the recently developed capsule networks, we propose a new matrix factorization model for collaborative filtering based on sparse linear capsule networks, which attempts to embed the neighborhood information into latent factors and finally get the very localized and global structure of a given rating matrix. Experiments on real-world datasets demonstrate that our model outperforms seven state-of-the-art matrix factorization-based CF methods in terms of rating prediction accuracy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chen, C., Li, D., Lv, Q., Yan, J., Shang, L., Chu, S.M.: Gloma: embedding global information in local matrix approximation models for collaborative filtering. In: Thirty-First AAAI Conference on Artificial Intelligence (2017)
Chen, C., Li, D., Zhao, Y., Lv, Q., Shang, L.: WEMAREC: accurate and scalable recommendation through weighted and ensemble matrix approximation. In: SIGIR, pp. 303–312 (2015)
Chen, J., Yu, H., Qian, C., Chen, D.Z., Wu, J.: A receptor skeleton for capsule neural networks. In: ICML (2021)
Dziugaite, G.K., Roy, D.M.: Neural network matrix factorization. arXiv preprint arXiv:1511.06443 (2015)
Hahn, T., Pyeon, M., Kim, G.: Self-routing capsule networks. In: NeurIPS (2019)
Hinton, G.E., Krizhevsky, A., Wang, S.: Transforming auto-encoders. In: ICANN (2011)
Hinton, G.E., Sabour, S., Frosst, N.: Matrix capsules with EM routing. In: ICLR (2018)
Jeong, T., Lee, Y., Kim, H.: Ladder capsule network. In: ICML (2019)
Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: KDD (2008)
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)
Lee, J., Kim, S., Lebanon, G., Singer, Y.: Local low-rank matrix approximation. In: International Conference on Machine Learning, pp. 82–90 (2013)
Li, D.S., Chen, C., Gong, Z., Lu, T., Chu, S., Gu, N.: Collaborative filtering with noisy ratings. In: SDM (2019)
Li, D., Chen, C., Lv, Q., Yan, J., Shang, L., Chu, S.: Low-rank matrix approximation with stability. In: International Conference on Machine Learning, pp. 295–303 (2016)
Mackey, L.W., Talwalkar, A., Jordan, M.I.: Divide-and-conquer matrix factorization. arXiv abs/1107.0789 (2011)
Malmgren, C.: A comparative study of routing methods in capsule networks (2019)
Mnih, A., Salakhutdinov, R.R.: Probabilistic matrix factorization. In: Advances in Neural Information Processing Systems, pp. 1257–1264 (2008)
Ning, X., Desrosiers, C., Karypis, G.: A comprehensive survey of neighborhood-based recommendation methods. In: Recommender Systems Handbook (2015)
Ning, X., Karypis, G.: Slim: sparse linear methods for top-n recommender systems. 2011 IEEE 11th International Conference on Data Mining, pp. 497–506 (2011)
Sabour, S., Frosst, N., Hinton, G.E.: Dynamic routing between capsules. arXiv abs/1710.09829 (2017)
Salakhutdinov, R., Mnih, A.: Bayesian probabilistic matrix factorization using Markov chain Monte Carlo. In: ICML 2008 (2008)
Srebro, N., Jaakkola, T.: Weighted low-rank approximations. In: Proceedings of the 20th International Conference on Machine Learning (ICML-2003), pp. 720–727 (2003)
Webb, B.: Netflix update: Try this at home (2006). Blog post: https://sifter.org/simon/journal/20061211.html
Yuan, T., Cheng, J., Zhang, X., Qiu, S., Lu, H.: Recommendation by mining multiple user behaviors with group sparsity. In: AAAI (2014)
Zhang, S., Yao, L., Xu, X.: AutoSVD++: an efficient hybrid collaborative filtering model via contractive auto-encoders. In: SIGIR, pp. 957–960. ACM (2017)
Acknowledgement
The work described in this paper was supported by the National Key Research and Development Program of China (No. 2019YFB1707001) and the National Natural Science Foundation of China (Grant No. 62021002).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Li, X., Zhang, L. (2023). Sparse Linear Capsules for Matrix Factorization-Based Collaborative Filtering. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds) Neural Information Processing. ICONIP 2022. Lecture Notes in Computer Science, vol 13623. Springer, Cham. https://doi.org/10.1007/978-3-031-30105-6_40
Download citation
DOI: https://doi.org/10.1007/978-3-031-30105-6_40
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-30104-9
Online ISBN: 978-3-031-30105-6
eBook Packages: Computer ScienceComputer Science (R0)