Abstract
In recent years, sequential recommender systems have been widely applied for service recommendations. However, most existing solutions do not take full advantage of one key factor that usually influences user behaviors: the category of services. It is necessary yet challenging to capture users’ category preferences. Firstly, the complex inherent relationships that exist among categories are vital but difficult to mine and encode. Secondly, since interest preferences and category preferences are closely related, their dynamic evolution has to be studied simultaneously. To tackle the above challenges, we propose a novel Reciprocal Dual-Channel Network (RDCN) to capture users’ comprehensive dynamic characteristics toward more accurate recommendations. For the former challenge, we devise a novel strategy to obtain the co-occurrence information of services and categories and jointly pre-train their embeddings. For the latter challenge, we design a Co-Guided Attention module and a Co-Guided GRU module to extract interest preferences and category preferences, respectively. Experimental results on three public datasets have demonstrated the necessity of exploiting the category information and the effectiveness of the proposed RDCN model.
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References
Carbaugh, R.: Contemporary Economics: an Applications Approach. Routledge, Oxfordshire, UK (2016)
Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)
Fan, X., Liu, Z., Lian, J., Zhao, W.X., Xie, X., Wen, J.R.: Lighter and better: low-rank decomposed self-attention networks for next-item recommendation. In: Proceedings of The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1733–1737 (2021)
Garcin, F., Dimitrakakis, C., Faltings, B.: Personalized news recommendation with context trees. In: Proceedings of The 7th ACM Conference on Recommender Systems, pp. 105–112 (2013)
Hidasi, B., Karatzoglou, A., Baltrunas, L., Tikk, D.: Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939 (2015)
Hidasi, B., Quadrana, M., Karatzoglou, A., Tikk, D.: Parallel recurrent neural network architectures for feature-rich session-based recommendations. In: Proceedings of The 10th ACM Conference on Recommender Systems, pp. 241–248 (2016)
Hou, Y., Hu, B., Zhang, Z., Zhao, W.X.: Core: Simple and effective session-based recommendation within consistent representation space. arXiv preprint arXiv:2204.11067 (2022)
Kang, W.C., McAuley, J.: Self-attentive sequential recommendation. In: Proceedings of IEEE International Conference on Data Mining (ICDM), pp. 197–206. IEEE (2018)
Li, M., Zhao, X., Lyu, C., Zhao, M., Wu, R., Guo, R.: Mlp4rec: A pure MLP architecture for sequential recommendations. arXiv preprint arXiv:2204.11510 (2022)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)
Rashed, A., Elsayed, S., Schmidt-Thieme, L.: CARCA: context and attribute-aware next-item recommendation via cross-attention. arXiv preprint arXiv:2204.06519 (2022)
Ren, P., Chen, Z., Li, J., Ren, Z., Ma, J., De Rijke, M.: RepeatNet: a repeat aware neural recommendation machine for session-based recommendation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 4806–4813 (2019)
Sun, F., et al.: Bert4rec: sequential recommendation with bidirectional encoder representations from transformer. In: Proceedings of The 28th ACM International Conference on Information and Knowledge Management, pp. 1441–1450 (2019)
Tan, Q., et al.: Sparse-interest network for sequential recommendation. In: Proceedings of The 14th ACM International Conference on Web Search and Data Mining, pp. 598–606 (2021)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
Wang, D., Cui, P., Zhu, W.: Structural deep network embedding. In: Proceedings of The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1225–1234 (2016)
Xu, C., et al.: Graph contextualized self-attention network for session-based recommendation. In: IJCAI, vol. 19, pp. 3940–3946 (2019)
Ying, H., et al.: Sequential recommender system based on hierarchical attention network. In: Proceedings of International Joint Conference on Artificial Intelligence (2018)
Zhang, M., Liu, J., Zhang, W., Deng, K., Dong, H., Liu, Y.: CSSR: a context-aware sequential software service recommendation model. In: Hacid, H., Kao, O., Mecella, M., Moha, N., Paik, H. (eds.) ICSOC 2021. LNCS, vol. 13121, pp. 691–699. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-91431-8_45
Zhang, T., et al.: Feature-level deeper self-attention network for sequential recommendation. In: Proceedings of International Joint Conferences on Artificial Intelligence, pp. 4320–4326 (2019)
Zhang, X., et al.: Price does matter! modeling price and interest preferences in session-based recommendation. arXiv preprint arXiv:2205.04181 (2022)
Zhou, K., et al.: S3-rec: self-supervised learning for sequential recommendation with mutual information maximization. In: Proceedings of The 29th ACM International Conference on Information & Knowledge Management, pp. 1893–1902 (2020)
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Xu, S., Xiang, Q., Fan, Y., Yan, R., Zhang, J. (2023). Exploiting Category Information in Sequential Recommendation. In: Monti, F., Rinderle-Ma, S., Ruiz Cortés, A., Zheng, Z., Mecella, M. (eds) Service-Oriented Computing. ICSOC 2023. Lecture Notes in Computer Science, vol 14419. Springer, Cham. https://doi.org/10.1007/978-3-031-48421-6_5
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DOI: https://doi.org/10.1007/978-3-031-48421-6_5
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