Assessing Explainable Recommendations from Knowledge Graph-based in an International Streaming Platform

• Claudia Suarez Mariscal UFRGS
• Bruno Santana Massena de Lima UFRGS
• Renata Galante UFRGS
• Weverton Cordeiro UFRGS

Resumo

Explainable recommendations can increase users’ confidence in the results provided by recommendation systems by providing justifications of why a certain item is recommended. In this way, the use of the Knowledge Graph (KG) guarantees an optimal organization of the data enabling one to trace the relationships between entities (users, recommended items, item attributes and features, and so on). Current proposals use different approaches such as embedding, connection, and propagation to deal with common problems that persist when generating recommendations, such as cold start or data lake. However, the complexity of recommendation models seems to increase when there is a large amount of data. In this work, we propose an analysis of the applicability of different frameworks based on knowledge graphs to obtain explanatory recommendations using a large dataset from an international streaming platform, with the idea of knowing the advantages and limitations of each approach to validate if complex models should really be used to obtain the best results. Through the experimentation of RippleNet, KGCN, KGAT, ECFKG, and DSKE, we focus on dataset structure, category-based, and refinement type of each framework. To conclude, we provide details on some general points of the evaluation of all frameworks using our dataset.

Palavras-chave: Explainable recommendation, Embedding-based model, Connection-based model, Propagation-based model, Knowledge Graph

Referências

Qingyao Ai, Vahid Azizi, Xu Chen, and Yongfeng Zhang. 2018. Learning heterogeneous knowledge base embeddings for explainable recommendation. Algorithms 11, 9 (2018), 137

Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko. 2013. Translating embeddings for modeling multi-relational data. Advances in neural information processing systems 26 (2013)

Yixin Cao, Xiang Wang, Xiangnan He, Zikun Hu, and Tat-Seng Chua. 2019. Unifying knowledge graph learning and recommendation: Towards a better understanding of user preferences. In The world wide web conference. 151–161

Chen Gao, Yu Zheng, Nian Li, Yinfeng Li, Yingrong Qin, Jinghua Piao, Yuhan Quan, Jianxin Chang, Depeng Jin, Xiangnan He, 2023. A survey of graph neural networks for recommender systems: Challenges, methods, and directions. ACM Transactions on Recommender Systems 1, 1 (2023), 1–51.

Qingyu Guo, Fuzhen Zhuang, Chuan Qin, Hengshu Zhu, Xing Xie, Hui Xiong, and Qing He. 2020. A survey on knowledge graph-based recommender systems. IEEE Transactions on Knowledge and Data Engineering 34, 8 (2020), 3549–3568

Jin Huang, Wayne Xin Zhao, Hongjian Dou, Ji-Rong Wen, and Edward Y Chang. 2018. Improving sequential recommendation with knowledge-enhanced memory networks. In The 41st international ACM SIGIR conference on research & development in information retrieval. 505–514.

Guoliang Ji, Shizhu He, Liheng Xu, Kang Liu, and Jun Zhao. 2015. Knowledge graph embedding via dynamic mapping matrix. In Proceedings of the 53rd annual meeting of the association for computational linguistics and the 7th international joint conference on natural language processing (volume 1: Long papers). 687–696

Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, and Xuan Zhu. 2015. Learning entity and relation embeddings for knowledge graph completion. In Proceedings of the AAAI conference on artificial intelligence, Vol. 29

Carolina Nery, Renata Galante, and Weverton Cordeiro. 2021. FIP-SHA - Finding Individual Profiles Through SHared Accounts. In Database and Expert Systems Applications: 32nd International Conference, DEXA 2021, Virtual Event, September 27–30, 2021, Proceedings, Part II. Springer-Verlag, Berlin, Heidelberg, 115–126. https://doi.org/10.1007/978-3-030-86475-0_12

Yizhou Sun, Jiawei Han, Xifeng Yan, Philip S Yu, and Tianyi Wu. 2011. Pathsim: Meta path-based top-k similarity search in heterogeneous information networks. Proceedings of the VLDB Endowment 4, 11 (2011), 992–1003.

Hongwei Wang, Fuzheng Zhang, Jialin Wang, Miao Zhao, Wenjie Li, Xing Xie, and Minyi Guo. 2018. Ripplenet: Propagating user preferences on the knowledge graph for recommender systems. In Proceedings of the 27th ACM international conference on information and knowledge management. 417–426.

Hongwei Wang, Fuzheng Zhang, Xing Xie, and Minyi Guo. 2018. DKN: Deep knowledge-aware network for news recommendation. In Proceedings of the 2018 world wide web conference. 1835–1844.

Hongwei Wang, Miao Zhao, Xing Xie, Wenjie Li, and Minyi Guo. 2019. Knowledge graph convolutional networks for recommender systems. In The world wide web conference. 3307–3313

Shoujin Wang, Liang Hu, Yan Wang, Xiangnan He, Quan Z Sheng, Mehmet A Orgun, Longbing Cao, Francesco Ricci, and Philip S Yu. 2021. Graph learning based recommender systems: A review. arXiv preprint arXiv:2105.06339 (2021)

Xiao Wang, Deyu Bo, Chuan Shi, Shaohua Fan, Yanfang Ye, and S Yu Philip. 2022. A survey on heterogeneous graph embedding: methods, techniques, applications and sources. IEEE Transactions on Big Data 9, 2 (2022), 415–436

Xiang Wang, Xiangnan He, Yixin Cao, Meng Liu, and Tat-Seng Chua. 2019. Kgat: Knowledge graph attention network for recommendation. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining. 950–958.

Shiwen Wu, Fei Sun, Wentao Zhang, Xu Xie, and Bin Cui. 2022. Graph neural networks in recommender systems: a survey. Comput. Surveys 55, 5 (2022), 1–37.

Xiao Yu, Xiang Ren, Yizhou Sun, Bradley Sturt, Urvashi Khandelwal, Quanquan Gu, Brandon Norick, and Jiawei Han. 2013. Recommendation in heterogeneous information networks with implicit user feedback. In Proceedings of the 7th ACM conference on Recommender systems. 347–350.

Fuzheng Zhang, Nicholas Jing Yuan, Defu Lian, Xing Xie, and Wei-Ying Ma. 2016. Collaborative Knowledge Base Embedding for Recommender Systems. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (San Francisco, California, USA) (KDD ’16). Association for Computing Machinery, New York, NY, USA, 353–362. https://doi.org/10.1145/2939672.2939673

Yongfeng Zhang, Qingyao Ai, Xu Chen, and Pengfei Wang. 2018. Learning over knowledge-base embeddings for recommendation. arXiv preprint arXiv:1803.06540 (2018)

Yongfeng Zhang, Xu Chen, 2020. Explainable recommendation: A survey and new perspectives. Foundations and Trends® in Information Retrieval 14, 1 (2020), 1–101.

Yuan Zhang, Xiaoran Xu, Hanning Zhou, and Yan Zhang. 2020. Distilling structured knowledge into embeddings for explainable and accurate recommendation. In Proceedings of the 13th international conference on web search and data mining. 735–743.