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
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Paper References:
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[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.
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[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
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[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.
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[4] Goyal P, Ferrara E. Graph embedding techniques, applications, and performance: Asurvey[J]. arXiv preprint arXiv:1705.02801, 2017.
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[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.
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[6] Zhao H, Yao Q, Song Y, et al. Learning with Heterogeneous Side InformationFusion for Recommender Systems[J]. arXiv preprint arXiv:1801.02411, 2018.
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[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.
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[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.
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[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.
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[10] Tran V C, Hwang D, Nguyen N T. Hashtag Recommendation Approach Based onContent and User Characteristics[J]. Cybernetics and Systems, 2018: 1-16.
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[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.
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[12] Chaudhari S, Azaria A, Mitchell T. An entity graph based Recommender System[J]. AI Communications, 2017, 30(2): 141-149.
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[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.
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[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.
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[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.
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[16] Catherine R, Mazaitis K, Eskenazi M, et al. Explainable Entity-based Recommendations with Knowledge Graphs[J]. arXiv preprint arXiv:1707.05254,2017.
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[17] Zhang Y, Ai Q, Chen X, et al. Learning over Knowledge-Base Embeddings for Recommendation[J]. arXiv preprint arXiv:1803.06540, 2018.
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[18] Das D, Sahoo L, Datta S. A Survey on Recommendation System[J]. InternationalJournal of Computer Applications, 2017, 160(7).
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[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.
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[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]
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[21] Wang H, Zhang F, Xie X, et al. DKN: Deep Knowledge-Aware Network for News Recommendation[J]. arXiv preprint arXiv:1801.08284, 2018.
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[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.
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[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.
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[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.
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[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.
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[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.
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[27] Bobadilla J, Ortega F, Hernando A, et al. Recommender systems survey[J]. Knowledge-based systems, 2013, 46: 109-132.
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[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.
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[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]
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