Abstract
This paper exploits both the user and item attribute information for sequential recommendation. Attribute information has been explored in a number of traditional recommendation systems and proved to be effective to enhance the recommend performance. However, existing sequential recommendation methods model latent sequence patterns only and neglect the attribute information.
In this paper, we propose a novel deep neural framework which exploits the item and user attribute information in addition to the sequential effects. Our method has two key properties. The first one is to integrate the item attributes into the sequential modeling of purchased items. The second one is to combine the user attributes with his/her preference representation. We conduct extensive experiments on a widely used real-world dataset. Results prove that our model outperforms the state-of-the-art sequential recommendation approaches.
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References
Chen, X., et al.: Sequential recommendation with user memory networks. In: Proceedings of WSDM, pp. 108–116. ACM (2018)
Guo, H., Tang, R., Ye, Y., Li, Z., He, X.: DeepFM: a factorization-machine based neural network for CTR prediction (2017). arXiv preprint arXiv:1703.04247
He, R., McAuley, J.: Fusing similarity models with Markov chains for sparse sequential recommendation. In: Proceedings of ICDM, pp. 191–200. IEEE (2016)
Hidasi, B., Karatzoglou, A., Baltrunas, L., Tikk, D.: Session-based recommendations with recurrent neural networks (2015). arXiv preprint arXiv:1511.06939
Li, J., Ren, P., Chen, Z., Ren, Z., Lian, T., Ma, J.: Neural attentive session-based recommendation. In: Proceedings of CIKM, pp. 1419–1428. ACM (2017)
Liu, Q., Wu, S., Wang, D., Li, Z., Wang, L.: Context-aware sequential recommendation. In: Proceedings of ICDM, pp. 1053–1058. IEEE (2016)
Quadrana, M., Karatzoglou, A., Hidasi, B., Cremonesi, P.: Personalizing session-based recommendations with hierarchical recurrent neural networks. In: Proceedings of RecSys, pp. 130–137. ACM (2017)
Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. In: Proceedings of UAI, pp. 452–461 (2009)
Rendle, S., Freudenthaler, C., Schmidt-Thieme, L.: Factorizing personalized Markov chains for next-basket recommendation. In: Proceedings of WWW, pp. 811–820. ACM (2010)
Shi, C., Zhang, Z., Luo, P., Yu, P.S., Yue, Y., Wu, B.: Semantic path based personalized recommendation on weighted heterogeneous information networks. In: Proceedings of CIKM, pp. 453–462. ACM (2015)
Tang, J., Wang, K.: Personalized top-n sequential recommendation via convolutional sequence embedding. In: Proceedings of WSDM, pp. 565–573 (2018)
Wang, P., Guo, J., Lan, Y., Xu, J., Wan, S., Cheng, X.: Learning hierarchical representation model for nextbasket recommendation. In: Proceedings of SIGIR, pp. 403–412. ACM (2015)
Wang, X., He, X., Nie, L., Chua, T.: Item silk road: recommending items from information domains to social users. In: Proceedings of SIGIR, pp. 185–194 (2017)
Wu, C.Y., Ahmed, A., Beutel, A., Smola, A.J., Jing, H.: Recurrent recommender networks. In: Proceedings of WSDM, pp. 495–503. ACM (2017)
Yu, F., Liu, Q., Wu, S., Wang, L., Tan, T.: A dynamic recurrent model for next basket recommendation. In: Proceedings of SIGIR, pp. 729–732. ACM (2016)
Yu, X., et al.: Personalized entity recommendation: a heterogeneous information network approach. In: Proceedings of WSDM, pp. 283–292. ACM (2014)
Zhang, Y., Ai, Q., Chen, X., Croft, W.B.: Joint representation learning for top-n recommendation with heterogeneous information sources. In: Proceedings of CIKM, pp. 1449–1458. ACM (2017)
Zhao, H., Yao, Q., Li, J., Song, Y., Lee, D.L.: Meta-graph based recommendation fusion over heterogeneous information networks. In: Proceedings of SIGKDD, pp. 635–644. ACM (2017)
Acknowledgment
The work described in this paper has been supported in part by the NSFC project (61572376).
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Sun, K., Qian, T. (2018). Exploiting User and Item Attributes for Sequential Recommendation. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11305. Springer, Cham. https://doi.org/10.1007/978-3-030-04221-9_33
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DOI: https://doi.org/10.1007/978-3-030-04221-9_33
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