Abstract
Session-based recommendation (SBR) aims to predict the next item based on short behavior sequences for anonymous users. Most of the current SBR methods consider the scenario that a session just consists of a series of items. However, the multiple item attributes can also reflect user behaviors and provide information for recommendation. In other words, a session in the real world should consist of items and multiple item attributes, which means that the session is heterogeneous. In this paper, we propose a novel method for the anonymous recommendation with heterogeneous item attributes, named CHSR. Firstly, we construct homogeneous session graph and heterogeneous global graph for heterogeneous sessions to map the relationships among different item attributes. Secondly, homo-view and hetero-view of these two kinds of graph encoders are proposed to capture both intra and inter patterns of heterogeneous sessions. Thirdly, a cross-view fusion strategy with consistency loss is introduced to integrate the heterogeneous attribute information by fusing the representations from the two-type views. Finally, the interest preference of anonymous users is represented from the above steps. Extensive experiments conducted on three large-scale real-world datasets demonstrate the superior performance of CHSR over the state-of-the-art methods.
This work was supported in part by the National Natural Science Foundation of China (61972268), and the Joint Innovation Foundation of Sichuan University and Nuclear Power Institute of China.
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References
Cui, Q., Wu, S., Liu, Q., Zhong, W., Wang, L.: Mv-rnn: A multi-view recurrent neural network for sequential recommendation. TKDE 32(2), 317–331 (2018)
Hidasi, B., Karatzoglou, A.: Recurrent neural networks with top-k gains for session-based recommendations. In: CIKM, pp. 843–852 (2018)
Hu, B., Shi, C., Zhao, W.X., Yu, P.S.: Leveraging meta-path based context for top- N recommendation with a neural co-attention model. In: KDD, pp. 1531–1540 (2018)
Huang, C., et al.: Knowledge-aware coupled graph neural network for social recommendation. In: AAAI, pp. 4115–4122 (2021)
Jannach, D., Ludewig, M.: When recurrent neural networks meet the neighborhood for session-based recommendation. In: RecSys, pp. 306–310 (2017)
Li, J., Ren, P., Chen, Z., Ren, Z., Lian, T., Ma, J.: Neural attentive session-based recommendation. In: CIKM, pp. 1419–1428 (2017)
Liu, Q., Wu, S., Wang, D., Li, Z., Wang, L.: Context-aware sequential recommendation. In: ICDM, pp. 1053–1058 (2016)
Liu, Q., Zeng, Y., Mokhosi, R., Zhang, H.: STAMP: short-term attention/memory priority model for session-based recommendation. In: KDD, pp. 1831–1839 (2018)
Pang, Y., et al.: Heterogeneous global graph neural networks for personalized session-based recommendation. In: WSDM, pp. 775–783 (2022)
Qiu, R., Li, J., Huang, Z., Yin, H.: Rethinking the item order in session-based recommendation with graph neural networks. In: CIKM, pp. 579–588 (2019)
Qiu, R., Li, J., Huang, Z., Yin, H.: Rethinking the item order in session-based recommendation with graph neural networks. In: CIKM (2019)
Rendle, S., Freudenthaler, C., Schmidt-Thieme, L.: Factorizing personalized markov chains for next-basket recommendation. In: WWW, pp. 811–820 (2010)
Sarwar, B.M., Karypis, G., Konstan, J.A., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: WWW, pp. 285–295 (2001)
Shani, G., Heckerman, D., Brafman, R.I.: An MDP-based recommender system. J. Mach. Learn. Res. 6, 1265–1295 (2005)
Sheu, H.S., Chu, Z., Qi, D., Li, S.: Knowledge-guided article embedding refinement for session-based news recommendation. In: TNNLS (2021)
Shi, C., Hu, B., Zhao, W.X., Yu, P.S.: Heterogeneous information network embedding for recommendation. TKDE 31(2), 357–370 (2019)
Sun, F., et al.: Bert4rec: Sequential recommendation with bidirectional encoder representations from transformer. In: CIKM, pp. 1441–1450 (2019)
Tan, Y.K., Xu, X., Liu, Y.: Improved recurrent neural networks for session-based recommendations. In: RecSys, pp. 17–22 (2016)
Vaswani, A., et al.: Attention is all you need. In: NIPS, pp. 5998–6008 (2017)
Velickovic, P., Fedus, W., Hamilton, W.L., Liò, P., Bengio, Y., Hjelm, R.D.: Deep graph infomax. In: ICLR (2019)
Wang, J., Ding, K., Zhu, Z., Caverlee, J.: Session-based recommendation with hypergraph attention networks. In: SDM, pp. 82–90 (2021)
Wang, S., Cao, L., Wang, Y., Sheng, Q.Z., Orgun, M.A., Lian, D.: A survey on session-based recommender systems. ACM Comput. Surv. 54(7), 1–38 (2021)
Wang, X., et al.: Heterogeneous graph attention network. In: WWW, pp. 2022–2032 (2019)
Wang, Z., Wei, W., Cong, G., Li, X.L., Mao, X.L., Qiu, M.: Global context enhanced graph neural networks for session-based recommendation. In: SIGIR, pp. 169–178 (2020)
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)
Xia, X., Yin, H., Yu, J., Shao, Y., Cui, L.: Self-supervised graph co-training for session-based recommendation. In: CIKM, pp. 2180–2190 (2021)
Xia, X., Yin, H., Yu, J., Wang, Q., Cui, L., Zhang, X.: Self-supervised hypergraph convolutional networks for session-based recommendation. In: AAAI, pp. 4503–4511 (2021)
Xu, C., et al.: Graph contextualized self-attention network for session-based recommendation. In: IJCAI, pp. 3940–3946 (2019)
Zhao, H., Yao, Q., Li, J., Song, Y., Lee, D.L.: Meta-graph based recommendation fusion over heterogeneous information networks. In: KDD, pp. 635–644 (2017)
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Wang, J., Duan, L., Ma, R., Zhang, Y., Luo, Z. (2023). CHSR: Cross-view Learning from Heterogeneous Graph for Session-Based 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_21
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