References
- S. Wu, F. Sun, W. Zhang, et al., “Graph neural networks in recommender systems: a survey,” ACM Computing Surveys, vol. 55, no. 5, May 2022, pp. 1–37, doi:10.1145/3519724.
- S. Wang, L. Cao, Y. Wang, et al., “A survey on session-based recommender systems,” ACM Computing Surveys (CSUR), vol. 54, no. 7, Aug. 2021, pp. 1–38, doi:10.1145/3460951.
- J. Li, Z. Ye, “Course recommendations in online education based on collaborative filtering recommendation algorithm,” Complexity, vol. 2020, Apr. 2020, Article ID 8813370, doi:10.1155/2020/8813370.
- P. K. Singh, R. Ahmed, I. S. Rajput, et al., “A comparative study on prediction approaches of itembased collaborative filtering in neighborhood-based recommendations,” Wireless Personal Communications, vol. 121, no. 6, Nov. 2021, pp. 857–877, doi:10.1007/s11265-021-01696-1.
- G. Piao, J. G. Breslin, “A study of the similarities of entity embeddings learned from different aspects of a knowledge base for item recommendations,” in Proceedings of the European Semantic Web Conference (ESWC 2018), Springer, Cham, June 2018, pp. 345– 359, doi:10.1007/978-3-319-93417-4_21.
- M. J. Pazani, D. Billsus, “Content-based recommendation systems,” in The Adaptive Web: Methods and Strategies of Web Personalization, Springer, Berlin, Heidelberg, May 2007, pp. 325–341, doi:10.1007/978-3-540-72079-9_10.
- H. Wang, F. Zhang, J. Wang, et al., “Ripplenet: Propagating user preferences on the knowledge graph for recommender systems,” in Proceedings of the 27th ACM International Conference on Information and Knowledge Management (CIKM 2018), ACM Press, Oct. 2018, pp. 417–426, doi:10.1145/3269206.3271764.
- W. Jiang, Y. Sun, “Social-RippleNet: Jointly modeling of ripple net and social information for recommendation,” Applied Intelligence, vol. 53, no. 3, Mar. 2023, pp. 3472–3487, doi:10.1007/s10489-021-03214-7.
- Y. Q. Wang, L. Y. Dong, Y. L. Li, et al., “Multitask feature learning approach for knowledge graph enhanced recommendations with RippleNet,” Plos One, vol. 16, no. 5, May 2021, e0251162, doi:10.1371/journal. pone. 0251162.
- H. Wang, F. Zhang, X. Xie, et al., “DKN: Deep knowledge-aware network for news recommendation,” in Proceedings of the 2018 World Wide Web Conference (WWW 2018), ACM Press, Apr. 2018, pp. 1835–1844, doi:10.1145/3178876.3186143.
- H. Wang, F. Zhang, M. Hou, et al., “Shine: Signed heterogeneous information network embedding for sentiment like prediction,” in Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining (WSDM 2018), ACM Press, Feb. 2018, pp. 592–600, doi:10.1145/3159652.3159668.
- X. Yu, X. Ren, Y. Sun, et al., “Personalized entity recommendation: A heterogeneous information network approach,” in Proceedings of the 7th ACM International Conference on Web Search and Data Mining (WSDM 2014), ACM Press, Feb. 2014, pp. 283–292, doi:10.1145/2556195.2556222.
- Y. Cao, X. Wang, X. He, et al., “Unifying knowledge graph learning and recommendation: Towards a better understanding of user preferences,” in The World Wide Web Conference (WWW 2019), ACM Press, May 2019, pp. 151–161, doi:10.1145/3308558.3313433.
- F. M. Harper, J. A. Konstan, “The MovieLens Datasets: History and Context,” ACM Transactions on Interactive Intelligent Systems, vol. 5, no. 4, Dec. 2016, Article No. 19, doi:10.1145/2827872.