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Knowledge-based recommendation models effectively alleviate the data sparsity issue leveraging the side information in the knowledge graph, and have achieved considerable performance.
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Wang, H., Zhao, M., Xie, X., Li, W., Guo, M.: Knowledge graph convolutional networks for recommender systems. In: The world wide web conference. pp. 3307–3313 (2019)
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Wang, X., He, X., Cao, Y., Liu, M., Chua, T.S.: Kgat: Knowledge graph attention network for recommendation. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining. pp. 950–958 (2019)
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Guo, Q., Zhuang, F., Qin, C., Zhu, H., Xie, X., Xiong, H., He, Q.: A survey on knowledge graph-based recommender systems. IEEE Transactions on Knowledge and Data Engineering 34
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2022
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Bao, K., Zhang, J., Zhang, Y., Wang, W., Feng, F., He, X.: Tallrec: An effective and efficient tuning framework to align large language model with recommendation. In: Proceedings of the 17th ACM Conference on Recommender Systems. RecSys ’23, ACM (Sep 2023). https://doi.org/10.1145/3604915.3608857, http://dx.doi.org/10.1145/3604915.3608857
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2023
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Zhang, J., Xie, R., Hou, Y., Zhao, W.X., Lin, L., Wen, J.R.: Recommendation as instruction following: A large language model empowered recommendation approach (2023)
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