Abstract
Factorization Machine (FM) and its many deep learning variants are widely used in Recommender Systems. Despite their success in many applications, there still remain inherent challenges. Most existing FM methods are incapable of capturing the similarity of features well and usually suffer from irrelevant features in terms of recommendation tasks. Hence, it is necessary to fully utilize the similarity interaction between different features. In this paper, we propose a Deep Neural Factorization Machine, named DNFM, which contains “wide” and “deep” parts based on Wide&Deep. In the wide part, we elaborately design a Dimension-weighted Factorization Machine (DwFM) to improve the original FM. DwFM assigns different weights vectors to feature interactions from different fields interactions. The “deep” part of DNFM is a neural network, which is used to capture the nonlinear information of the feature interactions. Then we unify both the wide and deep parts to comprehensive learn the feature interactions. Extensive experiments verify that DNFM can significantly outperform state-of-the-art methods on two well-known datasets.
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References
Cheng, H.T., Koc, L., Harmsen, J., et al.: Wide & deep learning for recommender systems, in: Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. ACM. pp. 7–10 (2016)
He, X., Chua, T.S.: Neural factorization machines for sparse predictive analytics. In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR). ACM. pp. 355–364 (2017)
Rendle, S.: Factorization machines. In: 2010 IEEE International Conference on Data Mining (ICDM), pp. 995–1000 (2010)
Guo, H., Tang, R., Ye, Y., et al.: Deepfm: a factorization- machine based neural network for ctr prediction. arXiv preprint arXiv:1703.04247 (2017)
Juan, Y., Zhuang, Y., Chin, W.S., et al.: Field-aware factorization machines for CTR prediction. In: Proceedings of the 10th ACM Conference on Recommender Systems, pp. 43–50 (2016)
Pan, J., Jian, X., Alfonso, L., et al.: Field-weighted factorization machines for click-through rate prediction in display advertising. In: International World Wide Web Conferences, pp. 1349–1357 (2018)
Xiao, J., Ye, H., He, X., Zhang, H., Wu, F., Chua, T.S.: Attentional factorization machines: Learning the weight of feature interactions via attention networks. arXiv preprint arXiv:1708.04617 (2017)
Shan, Y., Hoens, T.R., et al.: Deep crossing: Webscale modeling without manually crafted combinatorial features. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, pp. 255–262 (2016)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern recognition (CVPR), pp. 770–778 (2016)
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1929–1958 (2014)
He, X., Liao, L., Zhang, H., et al.: Neural collaborative filtering. In: Proceedings of the 26th International Conference on World Wide Web (WWW), pp. 173–182 (2017)
Rendle, S., Freudenthaler, C., Gantner, Z. et al.: BPR: Bayesian personalized ranking from implicit feedback. In: Conference on Uncertainty in Artificial Intelligence. AUAI Press, pp. 452–461 (2009)
Xiang, W., He, X., Nie, L., Chua, T.S.: Item silk road: recommending items from information domains to social users. ACM (2017)
Duchi, J., Hazan, E., Singer, Y.: Adaptive subgradient methods for online learning and stochastic optimization. J. Mach. Learn. Res. 12, 257–269 (2011)
Baltrunas, L., Church, K., Karatzoglou, A., Oliver, N.: Frappe: under- standing the usage and perception of mobile app recommendations in-the-wild. arXiv preprint arXiv:1505.03014 (2015)
Harper, F.M., Konstan, J.A.: The movielens datasets: history and context. ACM Trans. Interact. Intell. Syst. (TIIS) 5(4), 1–19 (2015)
Rendle, S.: Factorization machines with LIBFM. ACM Trans. Intell. Syst. Technol. 3, 1–22 (2012)
Acknowledgements
This work is supported by the National Key Research and Development Program of China under grants 2016QY01W0202, National Natural Science Foundation of China under grants U1836204, U1936108 and 62141205, and Major Projects of the National Social Science Foundation under grant 16ZDA092, and the Fund Project of XJPCC under grants 2022CB002-08 and 2019AB001.
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Zhang, R., Zhu, Z., Liu, C., Li, Y., Li, R. (2022). Deep Neural Factorization Machine for Recommender System. In: Memmi, G., Yang, B., Kong, L., Zhang, T., Qiu, M. (eds) Knowledge Science, Engineering and Management. KSEM 2022. Lecture Notes in Computer Science(), vol 13369. Springer, Cham. https://doi.org/10.1007/978-3-031-10986-7_22
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DOI: https://doi.org/10.1007/978-3-031-10986-7_22
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