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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2023, Vol. 57 Issue (8): 1527-1540    DOI: 10.3785/j.issn.1008-973X.2023.08.006
    
Research progress of recommendation system based on knowledge graph
Hui-xin WANG(),Xiang-rong TONG*()
School of Computer and Control Engineering, Yantai University, Yantai 264005, China
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Abstract  

Aiming at the problems of data sparsity, cold start, low interpretability of recommendation, and insufficient personalization in recommender system, the integration of knowledge graph into recommender system was analyzed. From the demand of recommender system, the concept of knowledge graph, and the integration approach of recommender system and knowledge graph, the problems of current recommender system and the solutions of recommender system after integrating knowledge graph were summarized. It was reviewed that, in recent years, the attention mechanism, neural network and reinforcement learning methods were combined, by which the principles of node trade-off, node integration, and paths exploring were used to make full use of the complex structural information in knowledge graph, so as to improve the satisfaction degree with the recommender system. The challenges and possible future development direction of the recommender system integrating the knowledge graph were put forward in terms of knowledge graph completeness, dynamics, availability of higher-order relationships, and the performance of the recommendation.

Key words： knowledge graph      recommendation system      collaborative filtering      attention mechanism      graph embedding     
Received: 29 July 2022      Published: 31 August 2023
CLC:  TP 391  
Fund:  国家自然科学基金资助项目(62072392, 61972360)；山东省重大科技创新工程资助项目(2019522Y020131)；烟台市重点实验室资助项目
Corresponding Authors: Xiang-rong TONG     E-mail: huixin_king@163.com;xr_tong@163.com
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Hui-xin WANG
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Cite this article:

Hui-xin WANG,Xiang-rong TONG. Research progress of recommendation system based on knowledge graph. Journal of ZheJiang University (Engineering Science), 2023, 57(8): 1527-1540.

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https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2023.08.006     OR     https://www.zjujournals.com/eng/Y2023/V57/I8/1527


融合知识图谱的推荐系统研究进展

针对推荐系统存在的数据稀疏、冷启动、推荐可解释性低、个性化不足等问题，分析知识图谱在推荐系统中的融入情况.从推荐系统的需求、知识图谱的相关概念、推荐系统与知识图谱的融合方式3个方面，对当前推荐系统存在的问题及推荐系统融合知识图谱后的解决方案进行概括. 总结近年来通过结合注意力机制、神经网络、强化学习方法，采用取舍节点、整合节点、探索路径等原理充分利用知识图谱中复杂结构信息，从而提升推荐系统满意度. 提出融合知识图谱的推荐系统所面临的知识图谱完备性、动态性、高阶关系可利用度以及推荐性能方面的挑战及未来可能的发展方向.


关键词： 知识图谱,  推荐系统,  协同过滤,  注意力机制,  图嵌入 
Fig.1 Content based recommendation algorithm
Fig.2 CF recommendation chart based on user and item
Fig.3 Common side information of recommendation system
Fig.4 Diagram of knowledge graph
Fig.5 Schematic diagram of knowledge graph based recommendation system
方法 年份 KG使用方式 融入算法模型
特征 路径 混合 CNN GNN GCN Att RL RNN MF
CKE[61] 2016 √ — — — — — — — — √
KTUP[11] 2019 √ — — — — — √ — — —
DER[62] 2019 √ — — √ — — √ — √ —
MKR[43] 2019 √ — — √ — — — — — √
KV-MN[63] 2018 √ — — — — — √ — √ —
PGPR[52] 2019 — √ — — — — — √ — —
KPRN[64] 2019 — √ — — — — √ — √ √
TMER[65] 2021 — √ — — — — √ — √ —
FMG[66] 2017 — √ — — — — — — — √
Ekar[67] 2019 — √ — — — — — √ — —
ERKM[13] 2021 — √ — — — — — √ — —
ReMR[68] 2022 — √ — — — — — — — —
RippleNet[69] 2018 — — √ — — — √ — — —
KGCN[70] 2019 — — √ — — √ √ — — —
KGAT[71] 2019 — — √ — √ — √ — — —
KGNN-LS[72] 2019 — — √ — √ — √ — — —
KERL[73] 2020 — — √ — — — — √ — —
KGIN[74] 2021 — — √ — √ — √ — — —
Mvin[75] 2020 — — √ — √ √ — — — —
CKAN[76] 2020 — — √ — √ — √ — — —
KRED[77] 2020 — — √ √ — — √ — — —
McHa[78] 2022 — — √ — — — — — — —
PeRN[79] 2021 — — √ √ — — — — √ —
KR-GCN[80] 2022 — — √ — — √ √ — — —
Tab.1 Comparison list of recommendation algorithms using different KG methods
Fig.6 Trans series translation model
Fig.7 Collaborative knowledge graph
Fig.8 Schematic diagram of attention network
Fig.9 Two-level perceptual domain of target user entity nodes in knowledge graph
Fig.10 Interaction between Agent and environment in reinforcement learning
[1]   AL-KUMAIM N H, HASSAN S H, SHABBIR M S, et al Exploring the inescapable suffering among postgraduate researchers: information overload perceptions and implications for future research[J]. International Journal of Information and Communication Technology Education (IJICTE), 2021, 17 (1): 19- 41
doi: 10.4018/IJICTE.2021010102
[2]   THOMPSON P Finding out about: a cognitive perspective on search engine technology and the WWW[J]. Information Retrieval, 2002, 5 (2/3): 269- 271
[3]   HUI B, ZHANG L, ZHOU X, et al Personalized recommendation system based on knowledge embedding and historical behavior[J]. Applied Intelligence, 2022, 52 (1): 954- 966
doi: 10.1007/s10489-021-02363-w
[4]   ADOMAVICIUS G, TUZHILIN A Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions[J]. IEEE Transactions on Knowledge and Data Engineering, 2005, 17 (6): 734- 749
doi: 10.1109/TKDE.2005.99
[5]   SU X, KHOSHGOFTAAR T M A survey of collaborative filtering techniques[J]. Advances in Artificial Intelligence, 2009, (12): 1- 19
[6]   KWON Y, LEE Y, RHU M, et al. Tensor casting: co-designing algorithm-architecture for personalized recommendation training[C]// 2021 IEEE International Symposium on High-Performance Computer Architecture (HPCA). Seoul: IEEE, 2021: 235-248.
[7]   CHEN Y, DAI Y, HAN X, et al Dig users’ intentions via attention flow network for personalized recommendation[J]. Information Sciences, 2021, 547: 1122- 1135
doi: 10.1016/j.ins.2020.09.007
[8]   SHEPITSEN A, GEMMELL J, MOBASHER B, et al. Personalized recommendation in social tagging systems using hierarchical clustering[C]// Proceedings of the 2008 ACM Conference on Recommender Systems. Lausanne: ACM, 2008: 259-266.
[9]   GUO G. Integrating trust and similarity to ameliorate the data sparsity and cold start for recommender systems[C]// Proceedings of the 7th ACM Conference on Recommender Systems. Hong Kong: ACM, 2013: 451-454.
[10]   HAWKINS D M The problem of overfitting[J]. Journal of Chemical Information and Computer Sciences, 2004, 44 (1): 1- 12
doi: 10.1021/ci0342472
[11]   CAO Y, WANG X, HE X, et al. Unifying knowledge graph learning and recommendation: towards a better understanding of user preferences[C]// The World Wide Web Conference. San Francisco: WWW, 2019: 151-161.
[12]   LI Z, LIU H, ZHANG Z, et al. Learning knowledge graph embedding with heterogeneous relation attention networks[J]. IEEE Transactions on Neural Networks and Learning Systems, 2021, 33(8):3961-3973.
[13]   XIE L, HU Z, CAI X, et al Explainable recommendation based on knowledge graph and multi-objective optimization[J]. Complex and Intelligent Systems, 2021, 7 (3): 1241- 1252
doi: 10.1007/s40747-021-00315-y
[14]   LIN J, ZHAO Y, HUANG W, et al Domain knowledge graph-based research progress of knowledge representation[J]. Neural Computing and Applications, 2021, 33 (2): 681- 690
doi: 10.1007/s00521-020-05057-5
[15]   SU X, KHOSHGOFTAAR T M. A survey of collaborative filtering techniques[J]. Advances in Artificial Intelligence, 2009, 4: 1-19.
[16]   HE X, LIAO L, ZHANG H, et al. Neural collaborative filtering[C]// Proceedings of the 26th International Conference on World Wide Web. Perth: WWW, 2017: 173-182.
[17]   KONSTAN J A, MILLER B N, MALTZ D, et al Grouplens: applying collaborative filtering to usenet news[J]. Communications of the ACM, 1997, 40 (3): 77- 87
doi: 10.1145/245108.245126
[18]   SCHAFER J B, FRANKOWSKI D, HERLOCKER J, et al. Collaborative filtering recommender systems[J]. The Adaptive Web: Methods and Strategies of Web Personalization, 2007: 291-324.
[19]   SARWAR B, KARYPIS G, KONSTAN J, et al. Item-based collaborative filtering recommendation algorithms[C]// Proceedings of the 10th International Conference on World Wide Web. Hong Kong: WWW, 2001: 285-295.
[20]   KOREN Y, RENDLE S, BELL R. Advances in collaborative filtering [EB/OL]. [2021-11-22]. https://link.springer.com/chapter/10.1007/978-1-0716-2197-4_3.
[21]   WANG F, ZHU H, SRIVASTAVA G, et al Robust collaborative filtering recommendation with user-item-trust records[J]. IEEE Transactions on Computational Social Systems, 2021, 9 (4): 986- 996
[22]   PAZZANI M J, BILLSUS D. Content-based recommendation systems[M]. The adaptive web: methods and strategies of web personalization. Berlin: Springer, 2007: 325-341.
[23]   THORAT P B, GOUDAR R M, BARVE S Survey on collaborative filtering, content-based filtering and hybrid recommendation system[J]. International Journal of Computer Applications, 2015, 110 (4): 31- 36
doi: 10.5120/19308-0760
[24]   DEBNATH S, GANGULY N, MITRA P. Feature weighting in content based recommendation system using social network analysis[C]// Proceedings of the 17th International Conference on World Wide Web. Beijing: WWW, 2008: 1041-1042.
[25]   WU L, HE X, WANG X, et al. A survey on neural recommendation: from collaborative filtering to content and context enriched recommendation [EB/OL]. [2021-12-27]. https://arxiv.org/abs/2104.13030v2.
[26]   LOPS P, JANNACH D, MUSTO C, et al Trends in content-based recommendation[J]. User Modeling and User-Adapted Interaction, 2019, 29 (2): 239- 249
doi: 10.1007/s11257-019-09231-w
[27]   KARN A L, KARNA R K, KONDAMUDI B R, et al Customer centric hybrid recommendation system for E-Commerce applications by integrating hybrid sentiment analysis[J]. Electronic Commerce Research, 2023, 23 (1): 279- 314
[28]   JUNG K Y, LEE J H User preference mining through hybrid collaborative filtering and content-based filtering in recommendation system[J]. Ieice Transactions on Information and Systems, 2004, 87 (12): 2781- 2790
[29]   DUONG T N, DO T G, DOAN N N, et al. Hybrid similarity matrix in neighborhood-based recommendation system[C]// 2021 8th NAFOSTED Conference on Information and Computer Science (NICS). Hanoi: IEEE, 2021: 475-480.
[30]   GUO Y, WANG M, LI X. An interactive personalized recommendation system using the hybrid algorithm model [EB/OL]. [2017-03-13]. https://www.mdpi.com/2073-8994/9/10/216.
[31]   NILASHI M, BIN IBRAHIM O, ITHNIN N Hybrid recommendation approaches for multi-criteria collaborative filtering[J]. Expert Systems with Applications, 2014, 41 (8): 3879- 3900
doi: 10.1016/j.eswa.2013.12.023
[32]   SONG Y, LIU S, JI W. Research on personalized hybrid recommendation system[C]// 2017 International Conference on Computer, Information and Telecommunication Systems (CITS). Dalian: IEEE, 2017: 133-137.
[33]   YING H, CHEN L, XIONG Y, et al. Collaborative deep ranking: a hybrid pair-wise recommendation algorithm with implicit feedback[C]// Pacific-asia Conference on Knowledge Discovery and Data Mining. Cham: Springer, 2016: 555-567.
[34]   SINGHAL A, SINHA P, PANT R. Use of deep learning in modern recommendation system: a summary of recent works [EB/OL]. [2017-12-20]. https://arxiv.org/abs/1712.07525.
[35]   DEGENNE A, FORSÉ M. Introducing social networks[M]. Sage: Thousand Oaks, 1999.
[36]   KONG X, SHI Y, YU S, et al Academic social networks: modeling, analysis, mining and applications[J]. Journal of Network and Computer Applications, 2019, 132: 86- 103
doi: 10.1016/j.jnca.2019.01.029
[37]   王立才, 孟祥武, 张玉洁 上下文感知推荐系统[J]. 软件学报, 2012, 23 (1): 1- 20
WANG Li-cai, MENG Xiang-wu, ZHANG Yu-jie Context-aware recommender systems[J]. Journal of Software, 2012, 23 (1): 1- 20
[38]   GAO Q, MA P. Graph neural network and context-aware based user behavior prediction and recommendation system research [EB/OL]. [2020-11-30]. https://www.hindawi.com/journals/cin/2020/8812370/.
[39]   陈克寒, 韩盼盼, 吴健 基于用户聚类的异构社交网络推荐算法[J]. 计算机学报, 2013, 36 (2): 349- 359
CHEN Ke-han, HAN Pan-pan, WU Jian User clustering based social network recommendation[J]. Chinese Journal of Computers, 2013, 36 (2): 349- 359
[40]   SHARMA L, GERA A A survey of recommendation system: research challenges[J]. International Journal of Engineering Trends and Technology (IJETT), 2013, 4 (5): 1989- 1992
[41]   WALTER F E, BATTISTON S, SCHWEITZER F A model of a trust-based recommendation system on a social network[J]. Autonomous Agents and Multi-Agent Systems, 2008, 16 (1): 57- 74
doi: 10.1007/s10458-007-9021-x
[42]   童向荣, 张伟, 龙宇. Agent主观信任的传递性[J]. 软件学报, 2012, 23(11): 2862-2870.
TONG Xiang-rong, ZHANG Wei, LONG Yu. Transitivity of agent subjective trust [J]. Journal of Software, 2012, 23 (11): 2862-2870.
[43]   WANG H, ZHANG F, ZHAO M, et al. Multi-task feature learning for knowledge graph enhanced recommendation[C]// The World Wide Web Conference. San Francisco: WWW, 2019: 2000-2010.
[44]   GUO Q, ZHUANG F, QIN C, et al A survey on knowledge graph-based recommender systems[J]. Scientia Sinica Informationis, 2020, 34 (8): 3549- 3568
[45]   SHA X, SUN Z, ZHANG J. Hierarchical attentive knowledge graph embedding for personalized recommendation [EB/OL]. [2021-07-08]. https://www.sciencedirect.com/science/article/abs/pii/S1567422321000430.
[46]   QIN C, ZHU H, ZHUANG F, et al A survey on knowledge graph-based recommender systems[J]. Scientia Sinica Informationis, 2020, 50 (7): 937- 956
doi: 10.1360/SSI-2019-0274
[47]   MEZNI H, BENSLIMANE D, BELLATRECHE L Context-aware service recommendation based on knowledge graph embedding[J]. IEEE Transactions on Knowledge and Data Engineering, 2021, 34 (11): 5225- 5238
[48]   KHAN N, MA Z, ULLAH A, et al Categorization of knowledge graph based recommendation methods and benchmark datasets from the perspectives of application scenarios: a comprehensive survey[J]. Expert Systems with Applications, 2023, 13 (2): 117737
[49]   ZHANG X, XIN X, LI D, et al. Variational reasoning over incomplete knowledge graphs for conversational recommendation [C]// Proceedings of the 16th ACM International Conference on Web Search and Data Mining. Birmingham: ACM, 2023: 231-239.
[50]   HU B, YE Y, ZHONG Y, et al TransMKR: translation-based knowledge graph enhanced multi-task point-of-interest recommendation[J]. Neurocomputing, 2022, 474: 107- 114
doi: 10.1016/j.neucom.2021.11.049
[51]   赵廷廷, 王喆, 卢奕南. 基于传播概率矩阵的异构信息网络表示学习[J]. 浙江大学学报: 工学版, 2019, 53(3): 548-554.
ZHAO Ting-ting, WANG Zhe, LU Yi-nan. Heterogeneous information network representation learning based on transition probability matrix (HINtpm)[J]. Journal of Zhejiang University: Engineering Science, 2019, 53(3): 548-554.
[52]   WANG X, JI H, SHI C, et al. Heterogeneous graph attention network[C]// The World Wide Web Conference. San Francisco: WWW, 2019: 2022-2032.
[53]   XIAN Y, FU Z, MUTHUKRISHNAN S, et al. Reinforcement knowledge graph reasoning for explainable recommendation[C]// Proceedings of the 42nd International Acm Sigir Conference on Research and Development in Information Retrieval. Paris: ACM, 2019: 285-294.
[54]   CATHERINE R, MAZAITIS K, ESKENAZI M, et al. Explainable entity-based recommendations with knowledge graphs. [EB/OL]. [2017-07-12]. https://arxiv.org/abs/1707.05254.
[55]   CAO Z, XU Q, YANG Z, et al. Geometry interaction knowledge graph embeddings [C]// AAAI Conference on Artificial Intelligence. Vancouver: AAAI, 2022: 5521-5529.
[56]   SHI C, LI Y, ZHANG J, et al A survey of heterogeneous information network analysis[J]. IEEE Transactions on Knowledge and Data Engineering, 2016, 29 (1): 17- 37
[57]   CELLUCCI C J, ALBANO A M, RAPP P E Comparative study of embedding methods[J]. Physical Review E, 2003, 67 (6): 066210
doi: 10.1103/PhysRevE.67.066210
[58]   WANG D, CUI P, ZHU W. Structural deep network embedding[C]// Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco: KDD, 2016: 1225-1234.
[59]   GONZÁLEZ L, HOGAN A. Modelling dynamics in semantic web knowledge graphs with formal concept analysis[C]// Proceedings of the 2018 World Wide Web Conference. Lyon: WWW, 2018: 1175-1184.
[60]   BONATTI P A, DECKER S, POLLERES A, et al. Knowledge graphs: new directions for knowledge representation on the semantic web (Dagstuhl seminar 18371)[C]// Dagstuhl Reports. Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik. Wadern: [s. n. ], 2019, 8(9).
[61]   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. San Francisco: KDD, 2016: 353-362.
[62]   CHEN X, ZHANG Y, QIN Z. Dynamic explainable recommendation based on neural attentive models[C]// Proceedings of the AAAI Conference on Artificial Intelligence. London: AAAI, 2019: 53-60.
[63]   HUANG J, ZHAO W X, DOU H, et al. Improving sequential recommendation with knowledge-enhanced memory networks[C]// The 41st International ACM SIGIR Conference on Research and Development in Information Retrieval. Michigan: SIGIR, 2018: 505-514.
[64]   WANG X, WANG D, XU C, et al. Explainable reasoning over knowledge graphs for recommendation[C]// Proceedings of the AAAI Conference on Artificial Intelligence. Hawaii: AAAI, 2019, 33(1): 5329-5336.
[65]   CHEN H, LI Y, SUN X, et al. Temporal meta-path guided explainable recommendation[C]// Proceedings of the 14th ACM International Conference on Web Search and Data Mining. Virtual: KDD, 2021: 1056-1064.
[66]   ZHAO H, YAO Q, LI J, et al. Meta-graph based recommendation fusion over heterogeneous information networks[C]// Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Halifax: KDD, 2017: 635-644.
[67]   SONG W, DUAN Z, YANG Z, et al. Explainable knowledge graph-based recommendation via deep reinforcement learning[EB/OL]. [2017-07-12]. https://arxiv.org/abs/1707.05254.
[68]   WANG X, LIU K, WANG D, et al. Multi-level recommendation reasoning over knowledge graphs with reinforcement learning[C]// Proceedings of the ACM Web Conference 2022. Lyon: WWW, 2022: 2098-2108.
[69]   WANG H, ZHANG F, WANG J, et al. Ripplenet: propaga-ting user preferences on the knowledge graph for recomm-ender systems[C]// Proceedings of the 27th ACM International Conference on Information and Knowledge Management. Torino: CIKM, 2018: 417-426.
[70]   WANG H , ZHAO M , XIE X , et al. Knowledge graph convolutional networks for recommender systems [C]// The World Wide Web Conference. San Francisco: WWW, 2019: 3307-3313.
[71]   WANG X, HE X, CAO Y, et al. KGAT: knowledge graph attention network for recommendation[C]// Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Anchorage: KDD, 2019: 950-958.
[72]   WANG H, ZHANG F, ZHANG M, et al. Knowledge-aware graph neural networks with label smoothness regularization for recommender systems[C]// Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Anchorage: KDD, 2019: 968-977. .
[73]   WANG P, FAN Y, XIA L, et al. KERL: a knowledge-guided reinforcement learning model for sequential recommendation[C]// Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. Xian: SIGIR, 2020: 209-218.
[74]   WANG X, HUANG T, WANG D, et al. Learning intents behind interactions with knowledge graph for recommendation[C]// Proceedings of the Web Conference 2021. Ljubljana: WWW, 2021: 878-887.
[75]   TAI C Y, WU M R, CHU Y W, et al. Mvin: learning multiview items for recommendation[C]// Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. Xi’an: SIGIR, 2020: 99-108.
[76]   WANG Z, LIN G, TAN H, et al. CKAN: collaborative knowledge-aware attentive network for recommender systems[C]// Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. Xi’an: SIGIR, 2020: 219-228.
[77]   LIU D, LIAN J, WANG S, et al. KRED: knowledge-aware document representation for news recommendations[C]// 14th ACM Conference on Recommender Systems. Brazil: ACM, 2020: 200-209.
[78]   WANG J, SHI Y, LI D, et al McHa: a multistage clustering-based hierarchical attention model for knowledge graph-aware recommendation[J]. World Wide Web, 2022, 25 (3): 1103- 1127
doi: 10.1007/s11280-022-01022-5
[79]   HUANG Y, ZHAO F, GUI X, et al Path-enhanced explainable recommendation with knowledge graphs[J]. World Wide Web, 2021, 24 (5): 1769- 1789
doi: 10.1007/s11280-021-00912-4
[80]   MA T, HUANG L, LU Q, et al. KR-GCN: knowledge-aware reasoning with graph convolution network for explainable recommendation[J]. ACM Transactions on Information Systems (TOIS), 2023, 41(1): 1-27.
[81]   RENDLE S Factorization machines with libfm[J]. ACM Transactions on Intelligent Systems and Technology (TIST), 2012, 3 (3): 1- 22
[82]   CHEN M, ZHANG W, ZHU Y, et al. Meta-knowledge transfer for inductive knowledge graph embedding[C]// Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. Madrid: ACM, 2022: 927-937.
[83]   ZHOU Z, WANG C, FENG Y, et al JointE: jointly utilizing 1D and 2D convolution for knowledge graph embedding[J]. Knowledge-Based Systems, 2022, 240: 108100
doi: 10.1016/j.knosys.2021.108100
[84]   CUADRAS C M, AREANS C, FORTIANA J Some computational aspects of a distance—based model for prediction[J]. Communications in Statistics-simulation and Computation, 1996, 25 (3): 593- 609
doi: 10.1080/03610919608813332
[85]   陈曦, 陈华钧, 张文 规则增强的知识图谱表示学习方法[J]. 情报工程,, 2017, 3 (1): 26- 34
CHEN Xi, CHEN Hua-jun, ZHANG Wen Rule-augmented representation learningapproach in knowledge graph[J]. Technology Intelligence Engineering, 2017, 3 (1): 26- 34
doi: 10.3772/j.issn.2095-915x.2017.01.003
[86]   BORDES A, USUNIER N, GARCIA-DURAN A , et al. Translating embeddings for modeling multi-relational data[J]. Advances in Neural Information Processing Systems, 2013, 26: 2787-2795.
[87]   ZHEN W , ZHANG J , FENG J , et al. Knowledge graph embedding by translating on hyperplanes[C]// National Conference on Artificial Intelligence. Québec: AAAI Press, 2014, 28(1): 1112-1119.
[88]   LIN H, LIU Y, WANG W, et al Learning entity and relation embeddings for knowledge resolution[J]. Procedia Computer Science, 2017, 108: 345- 354
doi: 10.1016/j.procs.2017.05.045
[89]   JI G , HE S , XU L , et al. Knowledge graph embedding via dynamic mapping matrix[C]// Meeting of the Association for Computational Linguistics and the International Joint Conference on Natural Language Processing. Beijing: ACL, 2015: 687-696.
[90]   王婷, 朱小飞, 唐顾. 基于知识增强的图卷积神经网络的文本分类[J]. 浙江大学学报: 工学版, 2022, 56(2): 322-328.
WANG Ting, ZHU Xiao-fei, TANG Gu. Knowledge-enhanced graph convolutional neural networks for text classification [J]. Journal of Zhejiang University: Engineering Science, 2022, 56(2): 322-328.
[91]   YU D, ZHU C, YANG Y, et al. Jaket: joint pre-training of knowledge graph and language understanding[C]// Proceedings of the AAAI Conference on Artificial Intelligence. London: AAAI, 2022, 36(10): 11630-11638.
[92]   YU X, REN X, SUN Y, et al. Personalized entity recommendation: a heterogeneous information network approach[C]// Proceedings of the 7th ACM International Conference on Web Search and Data Mining. New York: KDD, 2014: 283-292.
[93]   DONG C, JU X, MA Y. HRS: hybrid recommendation system based on attention mechanism and knowledge graph embedding[C]// IEEE/WIC/ACM International Conference on Web Intelligence. Melbourne: WI, 2021: 406-413.
[94]   LIU G, GUO J Bidirectional LSTM with attention mechanism and convolutional layer for text classification[J]. Neurocomputing, 2019, 337: 325- 338
doi: 10.1016/j.neucom.2019.01.078
[95]   CHEN Y, LIU L, PHONEVILAY V, et al Image super-resolution reconstruction based on feature map attention mechanism[J]. Applied Intelligence, 2021, 51 (7): 4367- 4380
doi: 10.1007/s10489-020-02116-1
[96]   陈成, 张皞, 李永强, 等 关系生成图注意力网络的知识图谱链接预测[J]. 浙江大学学报:工学版, 2022, 56 (5): 1025- 1034
CHEN Cheng, ZHANG Hao, LI Yong-qiang, et al Knowledge graph link prediction based on relational generative graph attention network[J]. Journal of Zhejiang University: Engineering Science, 2022, 56 (5): 1025- 1034
[97]   ZHANG S, TONG H, XU J, et al Graph convolutional networks: a comprehensive review[J]. Computational Social Networks, 2019, 6 (1): 1- 23
doi: 10.1186/s40649-019-0061-6
[98]   CHEN M, WEI Z, HUANG Z, et al. Simple and deep graph convolutional networks[C]// International Conference on Machine Learning. Vienna: PMLR, 2020: 1725-1735.
[99]   WU F, SOUZA A, ZHANG T, et al. Simplifying graph convolutional networks[C]// International Conference on Machine Learning. Long Beach: PMLR, 2019: 6861-6871.
[100]   WILLIAMS R J Simple statistical gradient-following algorithms for connectionist reinforcement learning[J]. Machine Learning, 1992, 8 (3): 229- 256
[101]   NOORIAN A, HAROUNABADI A, RAVANMEHR R A novel sequence-aware personalized recommendation system based on multidimensional information[J]. Expert Systems with Applications, 2022, 202: 117079
doi: 10.1016/j.eswa.2022.117079
[102]   潘承瑞, 何灵敏, 胥智杰, 等 融合知识图谱的双线性图注意力网络推荐算法[J]. 计算机工程与应用, 2021, 57 (1): 29- 37
PAN Cheng-rui, HE Ling-min, XU Zhi-jie, et al Fusion knowledge graph and bilinear graph attention network recommendation algorithm[J]. Computer Engineering and Application, 2021, 57 (1): 29- 37
doi: 10.3778/j.issn.1002-8331.2006-0141
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