Abstract
Sequential recommendation systems aim to predict users’ next actions based on the preferences learned from their historical behaviors. There are still fundamental challenges for sequential recommender. First, with the popularization of online services, recommender needs to serve both the warm- and cold-start users. However, most existing models depending on user-item interactions lose merits due to the difficulty of learning sequential dependencies with limited interactions. Second, users’ behaviors in their historical sequences are often implicit and complex due to the objective variability of reality and the subjective randomness of users’ intentions. It is difficult to capture the dynamic transition patterns from these user-item interactions. In this work, we propose a graph-based interpolation enhanced sequential recommender with deformable convolutional network (GISDCN). For cold-start users, we re-construct item sequences into a graph to infer users’ possible preferences. To capture the complex sequential dependencies, we employ the deformable convolutional network to generate more robust and flexible filters. Finally, we conduct comprehensive experiments and verify the effectiveness of our model. The experimental results demonstrate that GISDCN outperforms most of the state-of-the-art models at cold-start conditions.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Rendle, S., Freudenthaler, C., Schmidt-Thieme, L.: Factorizing personalized Markov chains for next-basket recommendation. In: WWW, pp. 811–820 (2010)
Tang, J., Wang, K.: Personalized Top-N sequential recommendation via convolutional sequence embedding. In: WSDM, pp. 565–573 (2018)
Yuan, F., Karatzoglou, A., Arapakis, I., et al.: A simple convolutional generative network for next item recommendation. In: WSDM, pp. 582–590 (2019)
Tanjim, M., Ayyubi, H., Cottrell, G.: DynamicRec: a dynamic convolutional network for next item recommendation. In: CIKM, pp. 2237–2240 (2020)
Hu, G., Zhang, Y., Yang, Q.: CoNet: collaborative cross networks for cross-domain recommendation. In: CIKM, pp. 667–676 (2018)
Yao, H., Liu, Y., Wei, W., et al.: Learning from multiple cities: a meta-learning approach for spatial-temporal prediction. In: WWW, pp. 2181–2191 (2019)
Du, Z., Wang, X., Yang, H., Zhou, J., Tang, J.: Sequential scenario-specific meta learner for online recommendation. In: KDD, pp. 2895–2904 (2019)
Wang, J., Ding, K., Caverlee, J.: Sequential recommendation for cold-start users with meta transitional learning. In: SIGIR, pp. 1783–1787 (2021)
Wu, S., et al.: Session-based recommendation with graph neural networks. In: AAAI, pp. 346–353 (2019)
Chang, J., et al.: Sequential recommendation with graph neural networks. In: SIGIR, pp. 378–387 (2021)
Hidasi, B., Karatzoglou, A.: Recurrent neural networks with top-k gains for session-based recommendations. In: CIKM, pp. 843–852 (2018)
Wu, C., Ahmed, A., Beutel, A., Smola, A., Jing, H.: Recurrent recommender networks. In: WSDM, pp. 495–503 (2017)
Wang, S., Hu, L., Wang, Y., Cao, L., et al.: Sequential recommender systems: challenges, progress and prospects. In: IJCAI, pp. 6332–6338 (2019)
Wu, S., Tang, Y., Zhu, Y., Wang, L., Xie, X., Tan, T.: Session-based recommendation with graph neural networks. In: AAAI, pp. 346–353 (2019)
Wang, Z., Wei, W., Cong, G., Li, X., et al.: Global context enhanced graph neural networks for session-based recommendation. In: SIGIR, pp. 169–178 (2020)
Li, Y., Tarlow, D., et al.: Gated graph sequence neural networks. In: ICLR (Poster) (2016)
Gao, H., Zhu, X., Lin, S., Dai, J.: deformable kernels: adapting effective receptive fields for object deformation. In: ICLR, pp. 1–15 (2020)
Liu, Y., Li, B., et al.: A Knowledge-aware recommender with attention-enhanced dynamic convolutional network. In: CIKM, pp. 1079–1088 (2021)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Zang, Y. et al. (2022). GISDCN: A Graph-Based Interpolation Sequential Recommender with Deformable Convolutional Network. In: Bhattacharya, A., et al. Database Systems for Advanced Applications. DASFAA 2022. Lecture Notes in Computer Science, vol 13246. Springer, Cham. https://doi.org/10.1007/978-3-031-00126-0_21
Download citation
DOI: https://doi.org/10.1007/978-3-031-00126-0_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-00125-3
Online ISBN: 978-3-031-00126-0
eBook Packages: Computer ScienceComputer Science (R0)