A Multi-source Information Graph-based Web Service Recommendation Framework for a Web Service Ecosystem
DOI:
https://doi.org/10.13052/jwe1540-9589.2183Keywords:
Web service recommendation, Web service ecosystem, multi-source information, deep learning, graph neural networks, attention mechanismAbstract
Web service recommendation remains a highly demanding yet challenging task in the field of services computing. In recent years, researchers have started to employ side information comprised in a heterogeneous Web service ecosystem to address the issues of data sparsity and cold start in Web service recommendation. Some recent works have exploited the deep learning techniques to learn user/Web service representations accumulating information from multiplex sources. However, we argue that they still struggle to utilize multi-source information in a discriminating, unified and flexible manner. To tackle this problem, this paper presents a novel multi-source information graph-based Web service recommendation framework (MGASR), which can automatically and efficiently extract multifaceted knowledge from the heterogeneous Web service ecosystem. Specifically, different node-type and edge-type dependent parameters are designed to model corresponding types of objects (nodes) and relations (edges) in the Web service ecosystem. We then leverage graph neural networks (GNNs) with an attention mechanism to construct a multi-source information neural network (MIN) layer, for mining diverse significant dependencies among nodes. By stacking multiple MIN layers, each node can be characterized by a highly contextualized representation due to capturing high-order multi-source information. As such, MGASR can generate representations with rich semantic information toward supporting Web service recommendation tasks. Extensive experiments conducted over three real-world Web service datasets demonstrate the superior performance of our proposed MGASR as compared to various baseline methods.
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Hossein Arabi, Vimala Balakrishnan, and Nor Liyana Mohd Shuib. A context-aware personalized hybrid book recommender system. Journal of Web Engineering, pages 405–428, 2020.
Bing Bai, Yushun Fan, Wei Tan, and Jia Zhang. DLTSR: A deep learning framework for recommendations of long-tail web services. IEEE Transactions on Services Computing, 13(1):73–85, 2017.
Khalid Benabbes, Khalid Housni, Ali El Mezouary, and Ahmed Zellou. Recommendation system issues, approaches and challenges based on user reviews. Journal of Web Engineering, 21(4):1017–1054, 2022.
Shuhui Chen, Yushun Fan, Wei Tan, Jia Zhang, Bing Bai, and Zhenfeng Gao. Service recommendation based on separated time-aware collaborative poisson factorization. Journal of Web Engineering, pages 595–618, 2017.
Debashis Das, Laxman Sahoo, and Sujoy Datta. A survey on recommendation system. International Journal of Computer Applications, 160(7):6–10, 2017.
Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, and Dawei Yin. Graph neural networks for social recommendation. In Proceedings of The World Wide Web Conference, pages 417–426, 2019.
Xinyu Fu, Jiani Zhang, Ziqiao Meng, and Irwin King. Magnn: Metapath aggregated graph neural network for heterogeneous graph embedding. In Proceedings of The World Wide Web Conference, pages 2331–2341, 2020.
Zhenfeng Gao, Yushun Fan, Xiu Li, Liang Gu, Cheng Wu, and Jia Zhang. Discovery and analysis about the evolution of service composition patterns. Journal of Web Engineering, 18(7):579–626, 2019.
Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, and Meng Wang. Lightgcn: Simplifying and powering graph convolution network for recommendation. In Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval, pages 639–648, 2020.
Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. Neural collaborative filtering. In Proceedings of The 26th International Conference on World Wide Web, pages 173–182, 2017.
Keman Huang, Yushun Fan, and Wei Tan. Recommendation in an evolving service ecosystem based on network prediction. IEEE Transactions on Automation Science and Engineering, 11(3):906–920, 2014.
R Kalpana, K Saruladha, and J Jayabharathy. Studying the performance of qos specific web service recommendation system using virtural regions. J. Web Eng., 15(5&6):397–411, 2016.
Wang-Cheng Kang and Julian McAuley. Self-attentive sequential recommendation. In Proceedings of IEEE International Conference on Data Mining, pages 197–206. IEEE, 2018.
Tingting Liang, Liang Chen, Jian Wu, Hai Dong, and Athman Bouguettaya. Meta-path based service recommendation in heterogeneous information networks. In Proceedings of International Conference on Service Oriented Computing, pages 371–386. Springer, 2016.
Rafael Moreno-Vozmediano, Rubén S Montero, and Ignacio M Llorente. Key challenges in cloud computing: enabling the future Internet of services. IEEE Internet Computing, 17(4):18–25, 2012.
Senthilselvan Natarajan, Subramaniyaswamy Vairavasundaram, Sivaramakrishnan Natarajan, and Amir H Gandomi. Resolving data sparsity and cold start problem in collaborative filtering recommender system using linked open data. Expert Systems with Applications, 149(3):113248, 2020.
Tian Qiu, Lei Li, and Pin Lin. Web service discovery with uddi based on semantic similarity of service properties. In Proceedings of International Conference on Semantics, Knowledge and Grid, pages 454–457. IEEE, 2007.
Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. BPR: Bayesian personalized ranking from implicit feedback. In Proceedings of The 25th Conference on Uncertainty in Artificial Intelligence, pages 452–461, 2009.
Michael Schlichtkrull, Thomas N Kipf, Peter Bloem, Rianne Van Den Berg, Ivan Titov, and Max Welling. Modeling relational data with graph convolutional networks. In Proceedings of European Semantic Web Conference, pages 593–607. Springer, 2018.
Sushmita Singh and Manvi Siwach. Handling heterogeneous data in knowledge graphs: A survey. Journal of Web Engineering, pages 1145–1186, 2022.
Fei Sun, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou, and Peng Jiang. BERT4Rec: Sequential recommendation with bidirectional encoder representations from transformer. In Proceedings of The 28th ACM International Conference on Information and Knowledge Management, pages 1441–1450, 2019.
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in Neural Information Processing Systems, 30:6000–6010, 2017.
Xiang Wang, Xiangnan He, Yixin Cao, Meng Liu, and Tat-Seng Chua. KGAT: Knowledge graph attention network for recommendation. In Proceedings of The 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 950–958, 2019.
Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, and Tat-Seng Chua. Neural graph collaborative filtering. In Proceedings of The 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 165–174, 2019.
Xiao Wang, Houye Ji, Chuan Shi, Bai Wang, Yanfang Ye, Peng Cui, and Philip S Yu. Heterogeneous graph attention network. In Proceedings of The World Wide Web Conference, pages 2022–2032, 2019.
Chunyu Wei, Yushun Fan, Jia Zhang, and Haozhe Lin. A-HSG: Neural attentive service recommendation based on high-order social graph. In Proceedings of IEEE International Conference on Web Services, pages 338–346. IEEE, 2020.
Xing Wu, Yushun Fan, Jia Zhang, Haozhe Lin, and Junqi Zhang. QF-RNN: QI-matrix factorization based rnn for time-aware service recommendation. In Proceedings of IEEE International Conference on Services Computing, pages 202–209. IEEE, 2019.
Xing Wu, Zhenfeng Gao, Yushun Fan, Xiu Li, Liang Gu, Jia Zhang, Chang Chen, Hao Zhang, and Qiang Wang. T-dses: A blockchain-powered trusted decentralized service eco-system. Journal of Web Engineering, pages 2199–2242, 2021.
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip. A comprehensive survey on graph neural networks. IEEE Transactions on Neural Networks and Learning Systems, 32(1):4–24, 2020.
Fenfang Xie, Liang Chen, Yongjian Ye, Zibin Zheng, and Xiaola Lin. Factorization machine based service recommendation on heterogeneous information networks. In Proceedings of IEEE International Conference on Web Services, pages 115–122. IEEE, 2018.
Fenfang Xie, Shenghui Li, Liang Chen, Yangjun Xu, and Zibin Zheng. Generative adversarial network based service recommendation in heterogeneous information networks. In Proceedings of IEEE International Conference on Web Services, pages 265–272. IEEE, 2019.
Xiaofei Xu, Quan Z Sheng, Liang-Jie Zhang, Yushun Fan, and Schahram Dustdar. From big data to big service. Computer, 48(07):80–83, 2015.
Ruyu Yan, Yushun Fan, Jia Zhang, Junqi Zhang, and Haozhe Lin. Service recommendation for composition creation based on collaborative attention convolutional network. In Proceedings of IEEE International Conference on Web Services, pages 397–405. IEEE, 2021.
Qi Yu, Zibin Zheng, and Hongbing Wang. Trace norm regularized matrix factorization for service recommendation. In Proceedings of IEEE International Conference on Web Services, pages 34–41. IEEE, 2013.
Chuxu Zhang, Dongjin Song, Chao Huang, Ananthram Swami, and Nitesh V Chawla. Heterogeneous graph neural network. In Proceedings of The 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 793–803, 2019.
Yiwen Zhang, Chunhui Yin, Qilin Wu, Qiang He, and Haibin Zhu. Location-aware deep collaborative filtering for service recommendation. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51(6):3796–3807, 2019.
Zibin Zheng, Hao Ma, Michael R Lyu, and Irwin King. QoS-aware web service recommendation by collaborative filtering. IEEE Transactions on Services Computing, 4(2):140–152, 2010.
