E-TUP:Joint Knowledge Graph Learning Recommendation Method Incorporating E-CP and TUP

doi:10.3778/j.issn.1002-8331.2211-0464

Abstract

Abstract: At present, most of the methods to introduce knowledge graphs into recommendation systems only introduce known surface knowledge graph entities, without predicting and mining the intrinsic relationships of the graphs, and thus cannot exploit the hidden relationships in the knowledge graphs. In this paper, the joint learning recommendation model E-TUP (enhance towards understanding of user preference) is proposed to address the above problem, and E-CP (enhance canonical polyadic) is used to complement the knowledge graph and deliver the complete information. A storage space negative sampling method is used to store and update high-quality negative triples with the training process to improve the quality of negative triples in the knowledge graph complementation. Experimental results on link prediction show that the storage-space approach improves the link prediction accuracy of the E-TUP model by up to 10.3% compared to existing models. Recommendation experiments on the MovieLens-1m and DBbook2014 datasets achieve the best results on several evaluation metrics, achieving up to 5.5% improvement, indicating that E-TUP can effectively exploit the hidden relationships in the knowledge graph to improve recommendation accuracy. Finally, the results of the recommendation experiments based on automotive maintenance data show that E-TUP can effectively recommend relevant knowledge.

Key words: knowledge graph, recommendation system, link prediction, joint learning, knowledge graph complement

摘要： 目前，大部分将知识图谱引入推荐系统的方法只是将已知的表层知识图谱实体进行引入，没有对图谱的内在关系进行预测和挖掘，因此无法利用知识图谱中的隐藏关系。针对上述问题，提出联合学习推荐模型E-TUP（enhance towards understanding of user preference），使用E-CP（enhance canonical polyadic）进行知识图谱补全并将完整信息进行传递。利用储存空间负采样方法，将优质负例三元组进行存储，并随训练过程进行更新，以提高知识图谱补全中负例三元组的质量。链接预测实验结果显示，储存空间方法使E-TUP模型链接预测准确率对比现有模型最高提升10.3%。在MovieLens-1m和DBbook2014数据集上进行推荐实验，在多个评价指标上取得最佳结果，对比现有模型实现最高5.5%的提升，表明E-TUP可以有效利用知识图谱中的隐藏关系提高模型推荐准确率。基于汽车维修数据进行推荐实验，结果表明E-TUP可以有效推荐相关知识。

关键词: 知识图谱, 推荐系统, 链接预测, 联合学习, 知识图谱补全

ZHAO Bo, WANG Yujia, NI Ji. E-TUP:Joint Knowledge Graph Learning Recommendation Method Incorporating E-CP and TUP[J]. Computer Engineering and Applications, 2024, 60(8): 99-109.

赵博, 王宇嘉, 倪骥. E-TUP：融合E-CP与TUP的联合知识图谱学习推荐方法[J]. 计算机工程与应用, 2024, 60(8): 99-109.

References

[1] 张明星, 张骁雄, 刘姗姗, 等. 利用知识图谱的推荐系统研究综述[J].计算机工程与应用, 2023, 59(4): 30-42.
ZHANG M X, ZHANG X X, LIU S S, et al. Review of recommendation systems using knowledge graph[J].Computer Engineering and Applications, 2023, 59(4): 30-42.
[2] 罗承天, 叶霞. 基于知识图谱的推荐算法研究综述[J]. 计算机工程与应用, 2023, 59(1): 49-60.
LUO C T, YE X. Survey on knowledge graph-based recommendation methods[J]. Computer Engineering and Applications, 2023, 59(1): 49-60.
[3] 徐有为, 张宏军, 程恺, 等.知识图谱嵌入研究综述[J].计算机工程与应用, 2022, 58(9): 30-50.
XU Y W, ZHANG H J, CHENG K, et al. Comprehensive survey on knowledge graph embedding[J]. Computer Engineering and Applications, 2022, 58(9): 30-50.
[4] BORDES A, USUNIER N, GARCIA-DURAN A, et al. Translating embeddings for modeling multi-relational data[C]//Proceedings of the 26th International Conference on Neural Information Processing Systems, 2013: 2787-2795.
[5] WANG Z, ZHANG J, FENG J, et al. Knowledge graph embedding by translating on hyperplanes[C]//Proceedings of the AAAI Conference on Artificial Intelligence, 2014: 1112-1119.
[6] LIN Y, LIU Z, SUN M, et al. Learning entity and relation embeddings for knowledge graph completion[C]//Proceedings of the 29th AAAI Conference on Artificial Intelligence, 2015: 2181-2187.
[7] WANG Z, LI J, LIU Z, et al. Text-enhanced representation learning for knowledge graph[C]//Proceedings of International Joint Conference on Artificial Intelligent, 2016: 14-27.
[8] NGUYEN D Q, SIRTS K, QU L, et al. STransE: a novel embedding model of entities and relationships in knowledge bases[C]//Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics, 2016: 460-466.
[9] BORDES A, GLOROT X, WESTON J, et al. A semantic matching energy function for learning with multi-relational data[J]. Machine Learning, 2014, 94(2): 233-259.
[10] LIU Q, JIANG H, EVDOKIMOV A, et al. Probabilistic reasoning via deep learning: neural association models[J]. arXiv:1603.07704, 2016.
[11] YANG B, YIH W, HE X, et al. Embedding entities and relations for learning and inference in knowledge bases[C]//Proceedings of the 3rd International Conference on Learning Representations, 2014: 129-139.
[12] TROUILLON T, WELBL J, RIEDEL S, et al. Complex embeddings for simple link prediction[C]//Proceedings of the International Conference on Machine Learning, 2016: 2071-2080.
[13] BALA?EVI? I, ALLEN C, HOSPEDALES T M. Tucker: tensor factorization for knowledge graph completion[C]//Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing, 2019: 5184-5193.
[14] HITCHCOCK F L. The expression of a tensor or a polyadic as a sum of products[J]. Studies in Applied Mathematics, 1927, 6(1): 164-189.
[15] 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, 2001: 285-295.
[16] RENDLE S. Factorization machines[C]//Proceedings of the 2010 IEEE International Conference on Data Mining, 2010: 995-1000.
[17] KOREN Y, BELL R, VOLINSKY C. Matrix factorization techniques for recommender systems[J]. Computer, 2009, 42(8): 30-37.
[18] RENDLE S, FREUDENTHALER C, GANTNER Z, et al. BPR: Bayesian personalized ranking from implicit feedback[C]//Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, 2009: 452-461.
[19] CHEN X, XU H, ZHANG Y, et al. Sequential recommendation with user memory networks[C]//Proceedings of the 11th ACM International Conference on Web Search and Data Mining, 2018: 108-116.
[20] CHENG Z, DING Y, ZHU L, et al. Aspect-aware latent factor model: rating prediction with ratings and reviews[C]//Proceedings of the 2018 World Wide Web Conference, 2018: 639-648.
[21] FENG F, HE X, WANG X, et al. Temporal relational ranking for stock prediction[J]. ACM Transactions on Information Systems (TOIS), 2019, 37(2): 1-30.
[22] CATHERINE R, COHEN W. Personalized recommendations using knowledge graphs: a probabilistic logic programming approach[C]//Proceedings of the 10th ACM Conference on Recommender Systems, 2016: 325-332.
[23] HE R, KANG W C, MCAULEY J. Translation-based recommendation[C]//Proceedings of the 11th ACM Conference on Recommender Systems, 2017: 161-169.
[24] PEROZZI B, AL-RFOU R, SKIENA S. Deepwalk: online learning of social representations[C]//Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2014: 701-710.
[25] TANG J, QU M, WANG M, et al. LINE: large-scale information network embedding[C]//Proceedings of the 24th International Conference on World Wide Web, 2015: 1067-1077.
[26] GROVER A, LESKOVEC J. node2vec: scalable feature learning for networks[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016: 855-864.
[27] 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, 2016: 353-362.
[28] PIAO G, BRESLIN J G. Transfer learning for item recommendations and knowledge graph completion in item related domains via a co-factorization model[C]//Proceedings of the European Semantic Web Conference, 2018: 496-511.
[29] ZHANG Y, AI Q, CHEN X, et al. Learning over knowledge-base embeddings for recommendation[J]. arXiv:1803.06540, 2018.
[30] CAO Y, WANG X, HE X, et al. Unifying knowledge graph learning and recommendation: towards a better understanding of user preferences[C]//Proceedings of the World Wide Web Conference, 2019: 151-161.
[31] MILLER G A. WordNet: a lexical database for English[J]. Communications of the ACM, 1995, 38(11): 39-41.
[32] DETTMERS T, MINERVINI P, STENETORP P, et al. Convolutional 2D knowledge graph embeddings[C]//Proceedings of the 32nd AAAI Conference on Artificial Intelligence, 2017: 91-110.
[33] REDDI S J, KALE S, KUMAR S. On the convergence of adam and beyond[C]//Proceedings of the 6th International Conference on Learning Representations, 2018: 81-90.
[34] SOCHER R, CHEN D, MANNING C D, et al. Reasoning with neural tensor networks for knowledge base completion[C]//Advances in Neural Information Processing Systems, 2013: 926-934.
[35] DONG X, GABRILOVICH E, HEITZ G, et al. Knowledge vault: a web-scale approach to probabilistic knowledge fusion[C]//Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2014: 601-610.
[36] NOIA T D, OSTUNI V C, TOMEO P, et al. Sprank: semantic path-based ranking for top-n recommendations using linked open data[J]. ACM Transactions on Intelligent Systems and Technology (TIST), 2016, 8(1): 1-34.
[37] PASZKE A, GROSS S, MASSA F, et al. Pytorch: an imperative style, high-performance deep learning library[C]//Advances in Neural Information Processing Systems, 2019: 18-30.