Abstract
Existing Web services recommendation models suffer from matrix sparsity and cold-start problems due to the limited number of user-service interactions and too little contextual information, so this paper introduces knowledge graph as auxiliary information to Web service recommendation to alleviate matrix sparsity and cold-start problems. And we propose the Web services recommendation based on Metapath-guided Graph Attention Network Model (WSR-MGAT) to fully exploit the structural information of the knowledge graph to improve the recommendation accuracy. Specifically, WSR-MGAT uses graph embedding method to obtain the initial embedding of entities and relationships. In order to obtain more closely related neighbors, we propose to use a distance-aware path sampling method to extract meta-path instances with closer relationships. Previous knowledge graph-based Web service recommendations do not make full use of the rich interaction information, which may lead to limited performance. To address this problem, this paper uses meta-paths to guide nodes to recursively aggregate higher-order neighbor information and use an attention mechanism to distinguish the importance of neighbors. Meanwhile, the semantic information of nodes under different meta-paths is fused to obtain a more comprehensive embedding of nodes. To verify the performance of the model, we use the real data crawled from the ProgrammableWeb platform to conduct multiple groups of experiments. The experimental results show that the WSR-MGAT improves over the strongest baselines w.r.t. Prec@10 by 4.6%; Recall@10 by 3.8%; NDCG@10 by 4.2% and F1@10 by 4.9%.







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References
Zou G, Jiang M, Niu S, Wu H, Pang S, Gan Y (2018) QoS-aware Web service recommendation with reinforced collaborative filtering. In: International conference on service-oriented computing. Springer, Cham, pp 430–445. https://doi.org/10.1007/978-3-030-03596-9_31
Gao W, Chen L, Wu J, Bouguettaya A (2016) joint modeling users, services, mashups, and topics for service recommendation. In: 2016 IEEE international conference on web services (ICWS), pp 260–267. https://doi.org/10.1109/ICWS.2016.41
Deng S, Wu H, Taheri J, Zomaya AY, Wu Z (2016) Cost performance driven service mashup: a developer perspective. IEEE Trans Parallel Distrib Syst 27(8):2234–2247. https://doi.org/10.1109/TPDS.2015.2482980
Wang X, Chen W, Yang Y, Zhang X, Feng Z (2021) A survey of research on knowledge graph partitioning algorithms. J Comput Sci 44(01):235–260
Ji G, He S, Xu L, Liu K, Zhao, J (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 (ACL), pp 687–696. https://doi.org/10.3115/v1/P15-1067
Zhang F, Yuan J, Lian D, Xie X, Ma W (2016) Collaborative knowledge base embedding for recommender systems. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining (KDD '16), pp 353–362
Guo S, Zhang X, Du Y (2021) Path planning of coastal ships based on optimized DQN reward function. J Mar Sci Eng 9(2):210
Wang H, Zhao M, Xie X (2019) Knowledge graph convolutional networks for recommender systems. In: Proceedings of the web conference (WWW), pp 3307–3313
Thomas N, Kipf, Welling M (2017) Semi-supervised classification with graph convolutional networks. In: International conference on learning representations (ICLR), Vancouver, BC, Canada
Defferrard M, Bresson X, Vandergheynst P (2016) Convolutional neural networks on graphs with fast localized spectral filtering. In: Neural information processing systems, Barcelona (NIPS), pp 3837–3845
Veličković P, Cucurull G, Casanova A (2018) Graph attention network. In: International conference on learning representations (ICLR). arXiv preprint arXiv:1710.10903
Hamilton WL, Ying R, Leskovec J (2017) Inductive representation learning on large graphs. In: Neural information processing systems (NIPS), pp 1024–1034
Wang X, Ji H, Shi C, Wang B, Cui P, Yu P, Ye Y (2019) Heterogeneous graph attention network. In: Proceedings of the web conference (WWW), pp 2022–2032. https://doi.org/10.1145/3308558
Fu X, Zhang J, Meng Z, King I (2020) MAGNN: metapath aggregated graph neural network for heterogeneous graph embedding. In: Proceedings of the web conference (WWW), pp 2331–2341. https://doi.org/10.1145/3366423.3380297
Li Z, Wang HA (2018) Weighted similarity measure based on meta structure in heterogeneous information networks. In: Pacific rim knowledge acquisition workshop. Springer, Cham, pp 271–281
Lin Y, Liu Z, Sun M (2015) Learning entity and relation embeddings for knowledge graph completion. In: Twenty-ninth AAAI conference on artificial intelligence (AAAI), pp 2181–2187
Dong Y, Chawla NV, Swami A (2017) metapath2vec: scalable representation learning for heterogeneous networks. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining (KDD), pp 135–144
Vaswani A, Shazeer N, Parmar N (2017) Attention is all you need. In: Neural information processing systems (NIPS), pp 5998–6008
Zhang P, Gisela M (2001) User expectations and rankings of quality factors in different Web site domains. Int J Electron Commer 6(2):9–33. https://doi.org/10.1080/10864415.2001.11044237
Koren Y, Bell R, Volinsky C (2009) Matrix factorization techniques for recommender systems. Computer 42(8):30–37. https://doi.org/10.1109/MC.2009.263
Botangen KA, Jian Yu, Sheng QZ, Han Y, Yongchareon S (2020) Geographic-aware collaborative filtering for Web service recommendation. Expert Syst Appl 151:113347. https://doi.org/10.1016/j.eswa.2020.113347
Hao Wu, Kun Y, Bo Li, Binbin Z, Ching-Hsien H (2018) Collaborative QoS prediction with context-sensitive matrix factorization. Future Gener Comput Syst 82:669–678. https://doi.org/10.1016/j.future.2017.06.020
Cheng T, Wen J, Xiong Q, Zeng J, Zhou W, Cai X (2019) Personalized Web service recommendation based on QoS prediction and hierarchical tensor decomposition. IEEE Access 7:62221–62230. https://doi.org/10.1109/ACCESS.2019.2909548
Zhu X, Jing XY, Wu D (2018) Similarity-maintaining privacy preservation and location-aware low-rank matrix factorization for QoS prediction based Web service recommendation. IEEE Trans Serv Comput 14(3):889–902. https://doi.org/10.1109/TSC.2018.2839741
Mezni H, Arab SA, Benslimane D et al (2020) An evolutionary clustering approach based on temporal aspects for context-aware service recommendation. J Ambient Intell Human Comput 11(1):119–138. https://doi.org/10.1007/s12652-018-1079-6
Blei DM, Ng AY, Jordan MI (2003) Latent Dirichlet allocation. J Mach Learn Res 3:993–1022
Yu L, Junxing Z, Yu PS (2016) Service recommendation based on topics and trend prediction. In: International conference on collaborative computing: networking, applications and worksharing. Springer, Cham, pp 343–352
Li H, Liu J, Cao B, Shi M (2018) Topic adaptive Web API recommendation method integrating multi-dimensional information. J Softw 29(11):3374–3387
Chen T, Liu J, Cao B, Peng Z, Wen Y, Li R (2018) Web service recommendation based on word embedding and topic model. In: IEEE international conference on parallel & distributed processing with applications, ubiquitous computing & communications, big data & cloud computing, social computing & networking, sustainable computing & communications. IEEE, pp 903–910
Shi M, Liu J, Zhou D, Tang Y (2018) A topic-sensitive method for mashup tag recommendation utilizing multi-relational service data. IEEE Trans Serv Comput 14(2):342–355
Cao Z, Qiao X, Jiang S, Zhang X (2019) An efficient knowledge-graph-based web service recommendation algorithm. Symmetry 11(3):392. https://doi.org/10.3390/sym11030392
Dang D, Chen C, Li H (2021) Deep knowledge-aware framework for web service recommendation. J Supercomput 77(12):1–25. https://doi.org/10.1007/s11227-021-03832-2
Wang X, Liu X, Liu J (2021) A novel knowledge graph embedding based API recommendation method for Mashup development. World Wide Web 24(3):869–894
Berg R, Kipf TN, Welling M (2017) Graph convolutional matrix completion. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining (KDD). arXiv preprint arXiv:1706.02263
Wang X, He X, Wang M (2019) Neural graph collaborative filtering. In: Proceedings of the 42nd international ACM SIGIR conference on research and development in information retrieval (SIGIR), pp 165–174
Wang H, Zhao M, Xie X, Li W, Guo M (2019) Knowledge graph convolutional networks for recommender systems. In: The world wide Web conference (WWW), pp 3307–3313
Wang X, He X, Cao Y (2019) KGAT: Knowledge graph attention network for recommendation. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining (KDD), pp 950–958
Shi C, Hu B, Zhao WX (2018) Heterogeneous information network embedding for recommendation. IEEE Trans Knowl Data Eng 31(2):357–370
Wang Z, Zhang J, Feng J, Chen Z (2014) Knowledge graph embedding by translating on hyperplanes. Proceedings of the AAAI Conference on Artificial Intelligence 28(1):1112–1119
Huang J, Zhao WX, Dou H (2018) Improving sequential recommendation with knowledge-enhanced memory networks. In: The 41st international ACM SIGIR conference on research & development in information retrieval, pp 505–514
Rendle S, Freudenthaler C, Gantner Z (2009) BPR: Bayesian personalized ranking from implicit feedback. In: UAI, pp 452–461
Kingma DP, Ba J (2015) Adam: a method for stochastic optimization. In: 3rd international conference on learning representations (ICLR), CA, USA
Li AQ, Ahmed A, Ravi S, Smola AJ (2014) Reducing the sampling complexity of topic models. In: The 20th ACM SIGKDD international conference on knowledge discovery and data mining (KDD), pp 891–900
Hu B, Shi C, Zhao W X (2018) Leveraging meta-path based context for top-n recommendation with a neural co-attention mode. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, pp 1531–1540
He X, Chua T-S (2017) Neural factorization machines for sparse predictive analytics. In: Proceedings of the 40th international ACM SIGIR conference on research and development in information retrieval (SIGIR), pp 355–364
Funding
This work is supported by the National Key R&D Program of China (Grant No. 2018YFB1601502) and the LiaoNing Revitalization Talents Program (Grant No. XLYC1902071).
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Li, X., Zhang, X., Wang, P. et al. Web services recommendation based on Metapath-guided graph attention network. J Supercomput 78, 12621–12647 (2022). https://doi.org/10.1007/s11227-022-04369-8
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DOI: https://doi.org/10.1007/s11227-022-04369-8