Abstract
This paper presents a comprehensive platform named AutoRec, which can help developers build effective and explainable recommender models all in one platform. It implements several well-known and state-of-art deep learning models in item recommendation scenarios, a AutoML framework with a package of search algorithms for automatically tuning of hyperparameters, and several instance-level interpretation methods to enable the explainable recommendation. The main advantage of AutoRec is the integration of AutoML and explainable AI abilities into the deep learning based recommender algorithms platform.
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References
Art. 22 gdpr automated individual decision-making, including profiling (2018). https://gdpr.eu/article-22-automated-individual-decision-making/
Deepctr (2019). https://deepctr-doc.readthedocs.io/en/latest/index.html
Neural network intelligence (2019). https://nni.readthedocs.io/en/latest/
Ancona, M., et al.: Towards better understanding of gradient-based attribution methods for deep neural networks. In: ICML (2018). https://github.com/marcoancona/DeepExplain
Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.R., Samek, W.: On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PloS One 10(7), e0130140 (2015)
Chen, C., Zhao, P., Li, L., Zhou, J., Li, X., Qiu, M.: Locally connected deep learning framework for industrial-scale recommender systems. In: Proceedings of the 26th International Conference on World Wide Web Companion, WWW 2017 Companion, pp. 769–770 (2017). https://doi.org/10.1145/3041021.3054227
Cheng, H.T., et al.: Wide & deep learning for recommender systems. In: Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, pp. 7–10. ACM (2016)
Graham, S., Min, J.K., Wu, T.: Microsoft recommenders: tools to accelerate developing recommender systems. In: RecSys 2019, pp. 542–543 (2019)
Guo, H., Tang, R., Ye, Y., Li, Z., He, X.: Deepfm: a factorization-machine based neural network for ctr prediction. arXiv preprint arXiv:1703.04247 (2017)
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, pp. 355–364. ACM (2017)
Kim, Y.: Convolutional neural networks for sentence classification. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1746–1751. Doha, Qatar, October 2014. https://doi.org/10.3115/v1/D14-1181
Lian, J., Zhou, X., Zhang, F., Chen, Z., Xie, X., Sun, G.: xdeepfm: combining explicit and implicit feature interactions for recommender systems. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1754–1763. ACM (2018)
Shrikumar, A., Greenside, P., Kundaje, A.: Learning important features through propagating activation differences. In: Proceedings of the 34th International Conference on Machine Learning, ICML 2017, vol. 70, pp. 3145–3153. JMLR.org (2017)
Shrikumar, A., Greenside, P., Shcherbina, A., Kundaje, A.: Not just a black box: Learning important features through propagating activation differences. CoRR (2016). http://arxiv.org/abs/1605.01713
Song, W., et al.: Autoint: automatic feature interaction learning via self-attentive neural networks. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, CIKM 2019, pp. 1161–1170. New York, NY, USA (2019). https://doi.org/10.1145/3357384.3357925
Sun, F., et al.: Bert4rec: sequential recommendation with bidirectional encoder representations from transformer. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, CIKM 2019, pp. 1441–1450, New York, NY, USA (2019). https://doi.org/10.1145/3357384.3357895
Wang, R., Fu, B., Fu, G., Wang, M.: Deep & cross network for ad click predictions. In: Proceedings of the ADKDD 2017, p. 12. ACM (2017)
Yu, Y., Qian, H., Hu, Y.Q.: Derivative-free optimization via classification. In: AAAI 2016 (2016)
Zhou, G., et al.: Deep interest network for click-through rate prediction. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1059–1068. ACM (2018)
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Cui, Q. et al. (2021). AutoRec: A Comprehensive Platform for Building Effective and Explainable Recommender Models. In: Dong, Y., Ifrim, G., Mladenić, D., Saunders, C., Van Hoecke, S. (eds) Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track. ECML PKDD 2020. Lecture Notes in Computer Science(), vol 12461. Springer, Cham. https://doi.org/10.1007/978-3-030-67670-4_35
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DOI: https://doi.org/10.1007/978-3-030-67670-4_35
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