Abstract
In e-commerce platforms, mining temporal characteristics in user behavior is conducive to recommend the right product for the user at the right time. Recently, recurrent neural networks (RNNs) based methods have achieved profitable performance in exploring temporal features, however, in complex e-commerce scenarios, user preferences changing over time have not been fully exploited. In order to fill the gap, we propose a novel representation for user preferences with the inspiration of a quantum concept, density matrix. It encodes a mixture of item subspaces and represents distribution of user preferences at one time stamp. Further, such a representation and RNNs are combined to form our proposed Density Matrix based Preference Evolution Networks (DMPENs). Experiments on Amazon datasets as well as real-world e-commerce datasets demonstrate the effectiveness of the proposed methods, which achieve rapid convergence and superior performance compared with the state-of-the-art methods in terms of AUC and accuracy.
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Basile, I., Tamburini, F.: Towards quantum language models. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 1840–1849 (2017)
Blacoe, W., Kashefi, E., Lapata, M.: A quantum-theoretic approach to distributional semantics. In: Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 847–857 (2013)
Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling (2014). arXiv preprint: arXiv:1412.3555
Dai, H., Wang, Y., Trivedi, R., Song, L.: Deep coevolutionary network: embedding user and item features for recommendation (2016). arXiv preprint: arXiv:1609.03675
He, R., McAuley, J.: Ups and downs: modeling the visual evolution of fashion trends with one-class collaborative filtering. In: Proceedings of the 25th International Conference on World Wide Web, pp. 507–517. International World Wide Web Conferences Steering Committee (2016)
Hidasi, B., Karatzoglou, A., Baltrunas, L., Tikk, D.: Session-based recommendations with recurrent neural networks (2016)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization (2014). arXiv preprint: arXiv:1412.6980
Li, Q., Li, J., Zhang, P., Song, D.: Modeling multi-query retrieval tasks using density matrix transformation. In: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 871–874. ACM (2015)
McAuley, J., Targett, C., Shi, Q., Van Den Hengel, A.: Image-based recommendations on styles and substitutes. In: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 43–52. ACM (2015)
Piwowarski, B., Frommholz, I., Lalmas, M., Van Rijsbergen, K.: What can quantum theory bring to information retrieval. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, pp. 59–68. ACM (2010)
Piwowarski, B., Lalmas, M.: A quantum-based model for interactive information retrieval. In: Azzopardi, L., et al. (eds.) ICTIR 2009. LNCS, vol. 5766, pp. 224–231. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-04417-5_20
Qu, Y., et al.: Product-based neural networks for user response prediction over multi-field categorical data (2018). arXiv preprint: arXiv:1807.00311
Sordoni, A., Nie, J.Y., Bengio, Y.: Modeling term dependencies with quantum language models for IR. In: Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 653–662. ACM (2013)
Van Rijsbergen, C.J.: The Geometry of Information Retrieval. Cambridge University Press, Cambridge (2004)
Veit, A., Kovacs, B., Bell, S., McAuley, J., Bala, K., Belongie, S.: Learning visual clothing style with heterogeneous dyadic co-occurrences. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4642–4650 (2015)
Werbos, P.J.: Backpropagation through time: what it does and how to do it. Proc. IEEE 78(10), 1550–1560 (1990)
Williams, R.J., Zipser, D.: A learning algorithm for continually running fully recurrent neural networks. Neural Comput. 1(2), 270–280 (1989)
Wu, C.Y., Ahmed, A., Beutel, A., Smola, A.J., Jing, H.: Recurrent recommender networks. In: Proceedings of the tenth ACM International Conference on Web Search and Data Mining, pp. 495–503. ACM (2017)
Xie, M., Hou, Y., Zhang, P., Li, J., Li, W., Song, D.: Modeling quantum entanglements in quantum language models (2015)
Zhang, P., Niu, J., Su, Z., Wang, B., Ma, L., Song, D.: End-to-end quantum-like language models with application to question answering (2018)
Zhang, Y., et al.: A quantum-inspired multimodal sentiment analysis framework. Theor. Comput. Sci. 752, 21–40 (2018)
Zhang, Y., et al.: Sequential click prediction for sponsored search with recurrent neural networks. In: Twenty-Eighth AAAI Conference on Artificial Intelligence, pp. 1369–1375 (2014)
Zhou, G., et al.: Deep interest network for click-through rate prediction. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1059–1068. ACM (2018)
Zhu, Y., et al.: What to do next: modeling user behaviors by time-LSTM. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI 2017, pp. 3602–3608 (2017)
Zhu, Y., et al.: A brand-level ranking system with the customized attention-GRU model. In: Proceedings of the Twenty-Seven International Joint Conference on Artificial Intelligence, IJCAI 2018 (2018)
Acknowledgement
This work is funded in part of the National Key R&D Program of China (2017YEF0111900), the National Natural Science Foundation of China (61876129), the National Natural Science Foundation of China (Key Program, U1636203), the Alibaba Innovation Research Foundation 2017 and the European Unions Horizon 2020 research and innovation programme under the Marie Skodowska-Curie grant agreement No. 721321. Part of the work was performed when Panpan Wang visited the Alibaba Inc. in 2018.
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Wang, P., Li, Z., Pan, X., Ding, D., Chen, X., Hou, Y. (2019). Density Matrix Based Preference Evolution Networks for E-Commerce Recommendation. In: Li, G., Yang, J., Gama, J., Natwichai, J., Tong, Y. (eds) Database Systems for Advanced Applications. DASFAA 2019. Lecture Notes in Computer Science(), vol 11447. Springer, Cham. https://doi.org/10.1007/978-3-030-18579-4_22
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