Abstract
In the field of e-commerce, recommendation systems can accurately provide users with products and services of potential interest, thereby enhancing users’ online shopping experience. “Explicit” feedback and “implicit” feedback are mostly studied in two relatively independent research fields. In the actual interaction process between users and commodities, there is a kind of signal between the two Monotonic dependence, that is, sparse and reliable explicit signals must imply dense and noisy implicit signals. In this paper, a special “monotonic behavior chain” structure is proposed to constrain the two signals, and a series of user-commodity interaction behaviors is mapped into a user-commodity multi-stage binary interaction diagram. The two feedback signals were combined and the complete interaction was simulated between the user and the product. Then a depth model framework GAERE was proposed based on the graph auto-encoder, which converts the matrix completion problem of the traditional recommendation system into the problem of graph link prediction. Four realistic data sets were applied to evaluate the effectiveness of the proposed method. The model shows competitiveness on standard collaborative filtering benchmarks. In addition, the application of graph convolutional network was further explored to process graph structure data in recommendation system from the perspective of user behavior intention.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Qiang, B., Lu, Y., Yang, M., et al.: sDeepFM: multi-scale stacking feature interactions for click-through rate prediction. Electronics 9(2), 350 (2020)
Qin, J., Ren, K., Fang, Y., et al. Sequential recommendation with dual side neighbor-based collaborative relation modeling. In: WSDM 2020: The Thirteenth ACM International Conference on Web Search and Data Mining. ACM (2020)
Parra, D., Karatzoglou, A., Amatriain, X., Yavuz, I.: Implicit feedback recommendation via implicit-to-explicit ordinal logistic regression mapping. In: CARS (2011)
Jawaheer, G., Weller, P., Kostkova, P.: Modeling user preferences in recommender systems: a classification framework for explicit and implicit user feedback. ACM Trans. Interact. Intell. Syst. 4(2), 26 (2014)
Duvenaud, D., Maclaurin, D., Aguilera-Iparraguirre, J., et al. Convolutional networks on graphs for learning molecular fingerprints. arXiv preprint arXiv:1509.09292 (2015)
Kipf, T.N., Welling, M.: Variational graph auto-encoders. In: NIPS Bayesian Deep Learning Work shop (2016)
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)
Mnih, A., Salakhutdinov, R.R.: Probabilistic matrix factorization. In: Advances in Neural Information Processing Systems, pp. 1257–1264 (2008)
Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: ICDM (2008)
Strub, F., Gaudel, R., Mary, J.: Hybrid recommender system based on autoencoders. In: Proceedings of the 1st Workshop on Deep Learning for Recommender Systems (2016)
Liu, N.N., Xiang, E.W., Zhao, M., Yang, Q.: Unifying explicit and implicit feedback for collaborative filtering. In: CIKM (2010)
Li, X., Chen, H.: Recommendation as link prediction in bipartite graphs: a graph kernel-based machine learning approach. Decis. Support Syst. 54(2), 880–890 (2013)
Li, Y., Tarlow, D., Brockschmidt, M., Zemel, R.: Gated graph sequence neural networks. In: ICLR (2016)
Jawaheer, G., Weller, P., Kostkova, P.: Modeling user preferences in recommender systems: a classification framework for explicit and implicit user feedback. ACM Trans. Interact. Intell. Syst. 2(4), 26 (2014)
Gurbanov, T., Ricci, F.: Action prediction models for recommender systems based on collaborative filtering and sequence mining hybridization. In: Proceedings of the Symposium on Applied Computing (2017)
Zhou, M., Ding, Z., Tang, J., Yin, D.: Micro behaviors: a new perspective in e-commerce recommender systems. In: WSDM. ACM (2018)
Wu, S., Tang, Y., Zhu, Y., et al.: Session-based recommendation with graph neural network on Artificial Intelligence. Proc. AAAI Conf. Artif. Intell. 33(01), 346–353 (2019)
Wang, H., et al. Knowledge-aware graph neural networks with label smoothness regularization for recommender systems. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (2019)
Zhao, L., et al.: T-gcn: a temporal graph convolutional network for traffic prediction. IEEE Trans. Intell. Transp. Syst. 21(9), 3848–3858 (2019)
Xu, F., et al.: Relation-aware graph convolutional networks for agent-initiated social e-commerce recommendation. Proceedings of the 28th ACM International Conference on Information and Knowledge Management (2019)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2017)
Hamilton, W.L., Ying, R., Leskovec, J.: Representation learning on graphs: methods and applications. IEEE Data Eng. Bull. 40(3), 52–74 (2017)
Kipf, T.N., Welling, M.: Variational graph auto-encoders. arXiv preprint arXiv:1611.07308 (2016)
Monti, F., et al. Geometric deep learning on graphs and manifolds using mixture model CNNs. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2017)
Zheng, Y., et al. A neural autoregressive approach to collaborative filtering. In: International Conference on Machine Learning. PMLR (2016)
Johnson, C.C.: Logistic matrix factorization for implicit feedback data. In: NIPS (2014)
Ying, R., et al. Graph convolutional neural networks for web-scale recommender systems. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (2018)
Zhou, F., Wu, H., Trajcevski, G., et al.: Semi-supervised trajectory understks. In: Proceedings of the AAAI Conference and with POI Attention for End-to-End Trip Recommendation. ACM Transactions on Spatial Algorithms and Systems (TSAS) (2020)
Sedhain, S., Menon, A.K., Sanner, S., et al. Autorec: autoencoders meet collaborative filtering. In: Proceedings of the 24th International Conference on World Wide Web, pp. 111–112 (2015)
Wan, M., McAuley, J.: Item recommendation on monotonic behavior chains. In: Proceedings of the 12th ACM Conference on Recommender Systems (2018)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Yang, T. (2021). Item Recommendation Based on Monotonous Behavior Chains. In: Zeng, J., Qin, P., Jing, W., Song, X., Lu, Z. (eds) Data Science. ICPCSEE 2021. Communications in Computer and Information Science, vol 1451. Springer, Singapore. https://doi.org/10.1007/978-981-16-5940-9_2
Download citation
DOI: https://doi.org/10.1007/978-981-16-5940-9_2
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-5939-3
Online ISBN: 978-981-16-5940-9
eBook Packages: Computer ScienceComputer Science (R0)