Abstract
In the book recommendation system, the relationship between users and books can be regarded as a bipartite graph. The user's interest preferences are mined from the graph through Collaborative Filtering recommendation method, and then use the graph convolution neural network to effectively aggregate the characteristics of users and books, so as to form the book recommendation content that user interest. However, the mining of user interest in the existing book recommendation system is always based on a single user preference, ignoring the diversity of user preferences. We propose a multi-preference book recommendation method based on graph convolution neural network to observe the potential reading interest of users when interacting with books. By capturing these reading interests, we can get more information about users’ preferences, so as to recommend books more in line with their preferences. We extract the recommendation dataset from the real scenario of Bohai University Library between May 2014 and May 2021, and evaluate our method on it. The experimental results show that our method effectively improves the performance of book recommendation.
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References
Wang, X., He, X., et al.: Neural graph collaborative filtering. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2019, July 2019, pp. 165–174 (2019)
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)
He, X., Liao, L., et al.: Neural collaborative filtering. In: Proceedings of the 26th International Conference on World Wide Web, WWW 2017, April 2017, pp. 173–182 (2017)
Guo, H., Tang, R., et al.: DeepFM: a factorization-machine based neural network for CTR prediction. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence, IJCAI 2017, August 2017, pp. 1725–1731 (2017)
Rendle, S.: Factorization machines. In: Proceedings of the 10th IEEE International Conference on Data Mining, ICDM 2010, December 2010, pp. 14–17 (2010)
Chen, J., Zhou, D., et al.: Closing the generalization gap of adaptive gradient methods in training deep neural networks. In: Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence, IJCAI 2020, pp. 3267–3275 (2020)
Cheng, H.T., Koc, L, et al.: Wide & deep learning for recommender systems. In: Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, DLRS 2016, September 2016, pp. 7–10 (2016)
Covington, P., Adams, J., Sargin, E.: Deep neural networks for YouTube recommendations. In: Proceedings of the 10th ACM Conference on Recommender Systems 2016, pp. 191–198 (2016)
Gao, C., Zheng Y., et al.: Graph neural networks for recommender systems: challenges, methods, and directions. In: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining, WSDM 2022, February 2022, pp. 1623–1625 (2022)
Wu, Z., Pan, S., et al.: A comprehensive survey on graph neural networks. IEEE Trans. Neural Netw. Learn. Syst. 32, 4–24 (2019)
Fan, W., Ma, Y., et al.: Graph neural networks for social recommendation. In: Proceedings of the World Wide Web Conference, WWW 2019, May 2019, pp. 417–426 (2019)
Shuman, D.I., Narang, S.K., et al.: The emerging field of signal processing on graphs: extending high-dimensional data analysis to networks and other irregular domains. IEEE Signal Process. Mag. 30(3), 83–98 (2013)
Defferrard, M., Bresson, X., Vandergheynst, P.: Convolutional neural networks on graphs with fast localized spectral filtering. In: Proceedings of the 30th International Conference on Neural Information Processing Systems, NIPS 2016, December 2016, pp. 3844–3852 (2016)
Chiang, W.L., Liu, X., et al.: Cluster-GCN: an efficient algorithm for training deep and large graph convolutional networks. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2019, July 2019, pp. 257–266 (2019)
Velikovi, P., Cucurull, G., et al.: Graph attention networks. In: Proceedings of the 6th International Conference on Learning Representations (2018)
Zhao, H., et al.: Multivariate time-series anomaly detection via graph attention network. In: IEEE International Conference on Data Mining 2020, pp. 269–279 (2020)
Song, W., Xiao, Z., et al.: Session-based social recommendation via dynamic graph attention networks. In: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, WSDM 2019, January 2019, pp. 555–563 (2019)
Chen, C., Zhang, M., et al.: Neural attentional rating regression with review-level explanations. In: Proceedings of the 2018 World Wide Web Conference, WWW 2018, April 2018, pp.1583–1592 (2018)
Yang, Z., Yang, D., et al.: Hierarchical attention networks for document classification. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp.1480–1489 (2016)
Louizos, C., Welling, M., Kingma, D.P.: Learning sparse neural networks through regularization. In: Proceedings of the 6th International Conference on Learning Representations (2019)
Wang, S., Tang, J., et al.: Exploring hierarchical structures for recommender systems. IEEE Trans. Knowl. Data Eng. 30, 1 (2018)
Salakhutdinov, R.: Probabilistic matrix factorization. In: Proceedings of the 20th International Conference on Neural Information Processing Systems, NIPS 2007 December 2007, pp.1257–1264 (2007)
Sedhain, S., Menon AK, et al.: AutoRec: autoencoders meet collaborative filtering. In: Proceedings of the 24th International Conference on World Wide Web Companion, WWW 2015, May 2015, pp.111–112 (2015)
Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2008, August 2008, pp. 426–434 (2008)
Li, C., Zhai, R., Zuo, F., Yu, J., Zhang, L.: Mixed multi-channel graph convolution network on complex relation graph. In: Xing, C., Fu, X., Zhang, Y., Zhang, G., Borjigin, C. (eds.) WISA 2021. LNCS, vol. 12999, pp. 497–504. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87571-8_43
Acknowledgements
This paper is partially supported by the National Natural Science Foundation of China under Grant No.61972053 and No.62172057, The Project is sponsored by ‘Liaoning BaiQianWan Talents Program’ under Grant No.2021921024, Master's innovation fund project of Bohai University YJC2021–053.
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Li, S., Xing, X., Liu, Y., Yang, Z., Niu, Y., Jia, Z. (2022). Multi-preference Book Recommendation Method Based on Graph Convolution Neural Network. In: Zhao, X., Yang, S., Wang, X., Li, J. (eds) Web Information Systems and Applications. WISA 2022. Lecture Notes in Computer Science, vol 13579. Springer, Cham. https://doi.org/10.1007/978-3-031-20309-1_46
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