Abstract
Bundle recommendation aims to accurately predict the probabilities of user interactions with bundles. Most existing effective methods learn the embeddings of users and bundles from user-bundle interaction view and user-item-bundle interaction view. However, they seldom leverage the recommendation difference caused by the distinct learning trends of two views when modeling user preferences. Meanwhile, such two view interaction graphs are typically tree-like. If the graph data with this structure is embedded in Euclidean space, it will lead to severe distortion problem. To this end, we propose a novel Hyperbolic Mutual Learning model for Bundle Recommendation (HyperMBR). The model encodes the entities (user, item, bundle) of the two view interaction graphs in hyperbolic space to learn their accurate representations. Furthermore, a mutual distillation based on hyperbolic distance is proposed to encourage the two views to transfer knowledge for increasingly improving the recommendation performance. Extensive empirical experiments on two real-world datasets confirm that our HyperMBR achieves promising results compared to state-of-the-art bundle recommendation methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Adcock, A.B., Sullivan, B.D., Mahoney, M.W.: Tree-like structure in large social and information networks. In: ICDM, pp. 1–10 (2013)
Bonnabel, S.: Stochastic gradient descent on riemannian manifolds. IEEE Trans. Autom. Control 58(9), 2217–2229 (2013)
Cao, D., Nie, L., He, X., Wei, X., Zhu, S., Chua, T.S.: Embedding factorization models for jointly recommending items and user generated lists. In: SIGIR, pp. 585–594 (2017)
Chami, I., Ying, Z., Ré, C., Leskovec, J.: Hyperbolic graph convolutional neural networks. In: NeurIPS, pp. 4869–4880 (2019)
Chang, J., Gao, C., He, X., Jin, D., Li, Y.: Bundle recommendation with graph convolutional networks. In: SIGIR, pp. 1673–1676 (2020)
Chen, L., Liu, Y., He, X., Gao, L., Zheng, Z.: Matching user with item set: collaborative bundle recommendation with deep attention network. In: IJCAI, pp. 2095–2101 (2019)
Chen, W., Fang, W., Hu, G., Mahoney, M.W.: On the hyperbolicity of small-world and treelike random graphs. Internet Math. 9(4), 434–491 (2013)
Chen, Y., et al.: Modeling scale-free graphs with hyperbolic geometry for knowledge-aware recommendation. In: WSDM, pp. 94–102 (2022)
Fréchet, M.: Les éléments aléatoires de nature quelconque dans un espace distancié. Annales de l’institut Henri Poincaré 10(4), 215–310 (1948)
Ganea, O., Bécigneul, G., Hofmann, T.: Hyperbolic neural networks. In: NeurIPS, pp. 5350–5360 (2018)
He, Y., Wang, J., Niu, W., Caverlee, J.: A hierarchical self-attentive model for recommending user-generated item lists. In: CIKM, pp. 1481–1490 (2019)
Hu, K., Li, L., Xie, Q., Liu, J., Tao, X.: What is next when sequential prediction meets implicitly hard interaction? In: CIKM, pp. 710–719 (2021)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: ICLR (2015)
Law, M., Liao, R., Snell, J., Zemel, R.: Lorentzian distance learning for hyperbolic representations. In: ICML, pp. 3672–3681 (2019)
Levy, S.V.F.: Flavors of geometry, vol. 31 (1997)
Linial, N., London, E., Rabinovich, Y.: The geometry of graphs and some of its algorithmic applications. Combinatorica 15(2), 215–245 (1995)
Liu, K., Natarajan, P.: WMRB: learning to rank in a scalable batch training approach. In: RecSys (2017)
Liu, Q., Nickel, M., Kiela, D.: Hyperbolic graph neural networks. In: NeurIPS, pp. 8228–8239 (2019)
Liu, Y., Xie, M., Lakshmanan, L.V.: Recommending user generated item lists. In: RecSys, pp. 185–192 (2014)
Ma, Y., He, Y., Zhang, A., Wang, X., Chua, T.: CrossCBR: cross-view contrastive learning for bundle recommendation. In: SIGKDD, pp. 1233–1241 (2022)
Ravasz, E., Barabási, A.L.: Hierarchical organization in complex networks. Phys. Rev. E 67(2), 026112 (2003)
Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: bayesian personalized ranking from implicit feedback. In: UAI, pp. 452–461 (2009)
Rendle, S., Freudenthaler, C., Schmidt-Thieme, L.: Factorizing personalized Markov chains for next-basket recommendation. In: WWW, pp. 811–820 (2010)
Sala, F., De Sa, C., Gu, A., Ré, C.: Representation tradeoffs for hyperbolic embeddings. In: ICML, pp. 4460–4469 (2018)
Sun, J., Cheng, Z., Zuberi, S., Pérez, F., Volkovs, M.: HGCF: hyperbolic graph convolution networks for collaborative filtering. In: WWW, pp. 593–601 (2021)
Tay, Y., Anh Tuan, L., Hui, S.C.: Latent relational metric learning via memory-based attention for collaborative ranking. In: WWW, pp. 729–739 (2018)
Tifrea, A., Bécigneul, G., Ganea, O.E.: Poincare glove: hyperbolic word embeddings. In: ICLR (2019)
Vijaikumar, M., Shevade, S., Murty, M.N.: GRAM-SMOT: top-n personalized bundle recommendation via graph attention mechanism and submodular optimization. In: ECML/PKDD, pp. 297–313 (2020)
Wang, L., Hu, F., Wu, S., Wang, L.: Fully hyperbolic graph convolution network for recommendation. In: CIKM, pp. 3483–3487 (2021)
Wang, P., Li, L., Xie, Q., Wang, R., Xu, G.: Social dual-effect driven group modeling for neural group recommendation. Neurocomputing 481, 258–269 (2022)
Wang, X., Liu, X., Liu, J., Wu, H.: Relational graph neural network with neighbor interactions for bundle recommendation service. In: ICWS, pp. 167–172 (2021)
Zhang, S., Chen, H., Ming, X., Cui, L., Yin, H., Xu, G.: Where are we in embedding spaces? In: SIGKDD, pp. 2223–2231 (2021)
Zhang, Y., Xiang, T., Hospedales, T.M., Lu, H.: Deep mutual learning. In: CVPR, pp. 4320–4328 (2018)
Zhao, S., Wei, W., Zou, D., Mao, X.: Multi-view intent disentangle graph networks for bundle recommendation. In: AAAI, pp. 4379–4387 (2022)
Acknowledgements
This work is supported in part by the National Natural Science Foundation of China (62276196) and the Key Research and Development Program of Hubei Province (2021BAA030).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Ke, H., Li, L., Wang, P., Yuan, J., Tao, X. (2023). Hyperbolic Mutual Learning for Bundle Recommendation. In: Wang, X., et al. Database Systems for Advanced Applications. DASFAA 2023. Lecture Notes in Computer Science, vol 13944. Springer, Cham. https://doi.org/10.1007/978-3-031-30672-3_28
Download citation
DOI: https://doi.org/10.1007/978-3-031-30672-3_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-30671-6
Online ISBN: 978-3-031-30672-3
eBook Packages: Computer ScienceComputer Science (R0)