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Introduction

Related researches on recommendation system using user-item profile builded by knowledge graphs.
The indexes before paper references are linked to the detailed descriptions.

Keywords:

knowledge graph, user-item profile, recommendation system

• Paper References:

  • [1] Cai H, Zheng V W, Chang K. A comprehensive survey of graph embedding:problems, techniques and applications[J]. IEEE Transactions on Knowledge andData Engineering, 2018.

  • [2] Oramas S, Ostuni V C, Noia T D, et al. Sound and music recommendation with knowledge graphs[J]. ACM Transactions on Intelligent Systems and Technology(TIST), 2017, 8(2): 21.1

  • [3] Palumbo E, Rizzo G, Troncy R. Entity2rec: Learning user-item relatedness from knowledge graphs for top-n item recommendation[C]//Proceedings of the EleventhACM Conference on Recommender Systems. ACM, 2017: 32-36.

  • [4] Goyal P, Ferrara E. Graph embedding techniques, applications, and performance: Asurvey[J]. arXiv preprint arXiv:1705.02801, 2017.

  • [5] Bellini V, Anelli V W, Di Noia T, et al. Auto-Encoding User Ratings via KnowledgeGraphs in Recommendation Scenarios[C]//Proceedings of the 2nd Workshop onDeep Learning for Recommender Systems. ACM, 2017: 60-66.

  • [6] Zhao H, Yao Q, Song Y, et al. Learning with Heterogeneous Side InformationFusion for Recommender Systems[J]. arXiv preprint arXiv:1801.02411, 2018.

  • [7] Qiu L, Gao S, Lyu Q, et al. A novel non-Gaussian embedding based model for recommender systems[J]. Neurocomputing, 2018, 278: 144-152.

  • [8] Katarya R, Verma O P. Efficient music recommender system using context graph and particle swarm[J]. Multimedia Tools and Applications, 2018, 77(2): 2673-2687.

  • [9] Kumar P, Reddy G R M. Friendship Recommendation System Using Topological Structure of Social Networks[M]//Progress in Intelligent Computing Techniques:Theory, Practice, and Applications. Springer, Singapore, 2018: 237-246.

  • [10] Tran V C, Hwang D, Nguyen N T. Hashtag Recommendation Approach Based onContent and User Characteristics[J]. Cybernetics and Systems, 2018: 1-16.

  • [11] Minkov E, Kahanov K, Kuflik T. Graph-based recommendation integrating rating history and domain knowledge: Application to on-site guidance of museum visitors[J]. Journal of the Association for Information Science and Technology, 2017,68(8): 1911-1924.

  • [12] Chaudhari S, Azaria A, Mitchell T. An entity graph based Recommender System[J]. AI Communications, 2017, 30(2): 141-149.

  • [13] Belém F M, Almeida J M, Gonçalves M A. A survey on tag recommendationmethods[J]. Journal of the Association for Information Science and Technology,2017, 68(4): 830-844.

  • [14] Tarus J K, Niu Z, Mustafa G. Knowledge-based recommendation: a review of ontology-based recommender systems for e-learning[J]. Artificial IntelligenceReview, 2017: 1-28.

  • [15] Musto C, Basile P, Lops P, et al. Introducing linked open data in graph-based recommender systems[J]. Information Processing & Management, 2017, 53(2):405-435.

  • [16] Catherine R, Mazaitis K, Eskenazi M, et al. Explainable Entity-based Recommendations with Knowledge Graphs[J]. arXiv preprint arXiv:1707.05254,2017.

  • [17] Zhang Y, Ai Q, Chen X, et al. Learning over Knowledge-Base Embeddings for Recommendation[J]. arXiv preprint arXiv:1803.06540, 2018.

  • [18] Das D, Sahoo L, Datta S. A Survey on Recommendation System[J]. InternationalJournal of Computer Applications, 2017, 160(7).

  • [19] Wei J, He J, Chen K, et al. Collaborative filtering and deep learning based recommendation system for cold start items[J]. Expert Systems with Applications,2017, 69: 29-39.

  • [20] Wang H, Zhang F, Wang J, et al. Ripple Network: Propagating User Preferences on the Knowledge Graph for Recommender Systems[J]. arXiv preprint arXiv:1803.03467, 2018.[Github Code]

  • [21] Wang H, Zhang F, Xie X, et al. DKN: Deep Knowledge-Aware Network for News Recommendation[J]. arXiv preprint arXiv:1801.08284, 2018.

  • [22] Zhang F, Yuan N J, Lian D, et al. Collaborative knowledge base embedding for recommender systems[C]//Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. ACM, 2016: 353-362.

  • [23] Tay Y, Tuan L A, Phan M C, et al. Multi-Task Neural Network for Non-discrete Attribute Prediction in Knowledge Graphs[C]//Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. ACM, 2017: 1029-1038.

  • [24] Catherine R, Cohen W. Personalized recommendations using knowledge graphs: A probabilistic logic programming approach[C]//Proceedings of the 10th ACM Conference on Recommender Systems. ACM, 2016: 325-332.

  • [25] Xiang L, Yuan Q, Zhao S, et al. Temporal recommendation on graphs via long-and short-term preference fusion[C]//Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2010: 723-732.

  • [26] Zhang Z K, Zhou T, Zhang Y C. Personalized recommendation via integrated diffusion on user–item–tag tripartite graphs[J]. Physica A: Statistical Mechanics and its Applications, 2010, 389(1): 179-186.

  • [27] Bobadilla J, Ortega F, Hernando A, et al. Recommender systems survey[J]. Knowledge-based systems, 2013, 46: 109-132.

  • [28] Sun Z, Yang J, Zhang J, et al. Recurrent knowledge graph embedding for effective recommendation[C]. /Proceedings of the 12th ACM Conference on Recommender Systems. ACM, 2018: 297-305.

  • [29] Wang X, Wang D, Xu C, et al. Explainable Reasoning over Knowledge Graphs for Recommendation[J]. arXiv preprint arXiv:1811.04540, 2018.[Github Code]

• Blog References:

  1. 如何将知识图谱特征学习应用到推荐系统？
  2. 【论文笔记和代码梳理】RippleNet：基于知识图谱的用户偏好传播
  3. 【知识图谱可解释性推荐】相关论文和代码（AAAI、CVPR、WSDM)
  4. 基于知识图谱路径推理的可解释推荐