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research-article

Location-based deep factorization machine model for service recommendation

Authors:

Victor S. ShengAuthors Info & Claims

Applied Intelligence, Volume 52, Issue 9

Pages 9899 - 9918

https://doi.org/10.1007/s10489-021-02998-9

Published: 01 July 2022 Publication History

Abstract

The era of everythingasaservice led to an explosion of services with similar functionalities on the internet. Quickly obtaining a high-quality service has become a research focus in the field of service recommendation. Studies show that quality of service (QoS) predictions are an effective way to discover services with high quality. However, sparse data and performance fluctuation challenge the accuracy and robustness of QoS prediction. To solve these two challenges, this paper proposes a location-based deep factorization machine model, namely LDFM, by employing information entropy and location projection of users and services. Particularly, our LDFM can be decomposed into three phases: i) extending a raw QoS dataset without introducing additional information, where LDFM projects the existing users (services) in the direction of their position vectors to increase the number of users (services) as well as the number of records that users invoke services; ii) mining a sufficient number of potential features behind the behaviors of users who invoke services, where LDFM employs a factorization machine to extract potential features of breadth with low dimensions (i.e., one and two dimensions) and utilizes deep learning to seek potential depth features with high dimensions; and iii) weighting extracted features within various dimensions, where LDFM employs information entropy to strengthen the positive effects of valid features while reducing the negative impacts generated by biased features. Our experimental results (including t-test analyses) show that our proposed LDFM always performs well under different user-service matrix densities and performs better than existing start-of-the-art methods in terms of the accuracy and robustness of QoS predictions.

References

[1]

Ghafouri SH, Hashemi SM, Hung PCK (2020) A Survey on Web Service QoS Prediction Methods. IEEE Trans Serv Comput:1–1.

[2]

Tong E, Niu W, Liu J (2021) A Missing QoS Prediction Approach via Time-aware Collaborative Filtering. IEEE Trans Serv Comput:1–14.

[3]

Yang Y, Zheng Z, Niu X, Tang M, Lu Y, Liao X (2018) A Location-Based Factorization Machine Model for Web Service QoS Prediction, pp 1–1.

[4]

Zheng Z, Xiaoli L, Tang M, Xie F, Lyu MR (2020) Web Service QoS Prediction via Collaborative Filtering: A Survey. IEEE Trans Serv Comput:1–18.

[5]

Zhu J, He P, Zheng Z, and Lyu MROnline QoS Prediction for Runtime Service Adaptation via Adaptive Matrix FactorizationIEEE Trans Parallel Distrib Syst201728102911-292410.1109/TPDS.2017.2700796

[6]

Zou G, Chen J, He Q, Li K-C, Zhang B, and Gan Y NDMF: Neighborhood-integrated deep matrix factorization for service QoS prediction IEEE Trans Netw Serv Manag 2020 17 4 2717-2730

[7]

Chen X, Liu X, Huang Z, Sun H (2010) Regionknn: a scalable hybrid collaborative filtering algorithm for personalized web service recommendation. In: 2010 IEEE International conference on web services. IEEE, pp 9–16

[8]

Chen X, Zheng Z, Yu Q, and Lyu MR Web service recommendation via exploiting location and qos information IEEE Trans Parallel Distrib Syst 2013 25 7 1913-1924

[9]

Chen Z, Shen L, and Li FYour neighbors are misunderstood: on modeling accurate similarity driven by data range to collaborative web service qos predictionFutur Gener Comput Syst201995404-419https://doi.org/10.1016/j.future.2019.01.003

[10]

Guo H, Tang R, Ye Y, Li Z, He X (2017) Deepfm: a factorization-machine based neural network for ctr prediction. In: Proceedings of the twenty-sixth international joint conference on artificial intelligence, pp 1725–1731

[11]

He X, Du X, Wang X, Tian F, Tang J, Chua TS (2018) Outer product-based neural collaborative filtering. In: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, pp 2227–2233

[12]

He X, Liao L, Zhang H, Nie L, Hu X, Chua TS (2017) Neural collaborative filtering. In: Proceedings of the 26th international conference on world wide web, pp 173–182

[13]

Jin X, Xu C, Feng J, Wei Y, Xiong J, Yan S (2016) Deep learning with s-shaped rectified linear activation units. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 30

[14]

Liu J, Tang M, Zheng Z, Liu X, and Lyu S Location-aware and personalized collaborative filtering for web service recommendation IEEE Trans Serv Comput 2015 9 5 686-699

[15]

Mnih A, Salakhutdinov RR (2008) Probabilistic matrix factorization. In: Advances in neural information processing systems, pp 1257–1264

[16]

Rendle S (2010) Factorization machines. In: 2010 IEEE International conference on data mining. IEEE, pp 995–1000

[17]

Rendle S, Gantner Z, Freudenthaler C, Schmidt-Thieme L (2011) Fast context-aware recommendations with factorization machines. In: Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval, pp 635–644

[18]

Sarwar B, Karypis G, Konstan J, Riedl J (2001) Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th international conference on World Wide Web, pp 285–295

[19]

Shao L, Zhang J, Wei Y, Zhao J, Xie B, Mei H (2007) Personalized qos prediction forweb services via collaborative filtering. In: IEEE International conference on web services (ICWS 2007), pp 439–446.

[20]

Shao L, Zhang J, Wei Y, Zhao J, Xie B, Mei H (2007) Personalized qos prediction forweb services via collaborative filtering. In: IEEE International conference on web services (icws 2007), pp 439–446. IEEE

[21]

Tang J, Wang K (2018) Personalized top-n sequential recommendation via convolutional sequence embedding. In: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, pp 565–573

[22]

Tang M, Jiang Y, Liu J, Liu X (2012) Location-aware collaborative filtering for qos-based service recommendation. In: 2012 IEEE 19Th international conference on web services. IEEE, pp 202–209

[23]

Tang M, Zhang T, Yang Y, Zheng Z, and Cao B Qos-aware web service recommendation based on factorization machines Chin J Comput 2018 41 6 1300-1313

[24]

Wang Y A formal model of qos-aware web service orchestration engine IEEE Trans Netw Serv Manag 2015 13 1 113-125

[25]

Wu S, Ren W, Yu C, Chen G, Zhang D, Zhu J (2016) Personal recommendation using deep recurrent neural networks in netease. In: 2016 IEEE 32Nd international conference on data engineering (ICDE)9. IEEE, pp 1218–122

[26]

Wu Y, Xie F, Chen L, Chen C, Zheng Z (2017) An embedding based factorization machine approach for web service qos prediction. In: International conference on service-oriented computing. Springer, pp 272–286

[27]

Xu J, Zheng Z, and Lyu MRWeb service personalized quality of service prediction via reputation-based matrix factorizationIEEE Trans Reliab201665128-3710.1109/TR.2015.2464075

[28]

Xue HJ, Dai X, Zhang J, Huang S, Chen J (2017) Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, Melbourne, pp 3203–3209

[29]

Yang Y, Zheng Z, Niu X, Tang M, Lu Y, Liao X (2018) A location-based factorization machine model for web service qos prediction. IEEE Transactions on Services Computing

[30]

Zhang S, Yao L, Sun A, and Tay Y Deep learning based recommender system: a survey and new perspectives ACM Comput Surv (CSUR) 2019 52 1 1-38

[31]

Zhang Y, Wang K, He Q, Chen F, Deng S, Zheng Z, Yang Y (2019) Covering-based web service quality prediction via neighborhood-aware matrix factorization. IEEE Transactions on Services Computing

[32]

Zhang Y, Yin C, Wu Q, He Q, Zhu H (2019) Location-aware deep collaborative filtering for service recommendation. IEEE Transactions on Systems, Man, and Cybernetics, Systems

[33]

Zheng Z, Ma H, Lyu MR, and King I Qos-aware web service recommendation by collaborative filtering IEEE Trans Serv Comput 2010 4 2 140-152

[34]

Zheng Z, Zhang Y, Lyu MR (2010) Distributed qos evaluation for real-world web services. In: 2010 IEEE International conference on web services. IEEE, pp 83–90

Cited By

Zhang HWu MFeng QLi H(2024)AERQP: adaptive embedding representation-based QoS prediction for web service recommendationThe Journal of Supercomputing10.1007/s11227-023-05582-980:3(3042-3065)Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1007/s11227-023-05582-9
Mecheri KKlai SSouici-Meslati L(2023)Deep learning based web service recommendation methodsJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-22456544:6(9879-9899)Online publication date: 1-Jun-2023
https://dl.acm.org/doi/10.3233/JIFS-224565

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Information & Contributors

Information

Published In

cover image Applied Intelligence

Applied Intelligence Volume 52, Issue 9

Jul 2022

1230 pages

ISSN:0924-669X

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© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 July 2022

Accepted: 10 November 2021

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Cited By

Zhang HWu MFeng QLi H(2024)AERQP: adaptive embedding representation-based QoS prediction for web service recommendationThe Journal of Supercomputing10.1007/s11227-023-05582-980:3(3042-3065)Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1007/s11227-023-05582-9
Mecheri KKlai SSouici-Meslati L(2023)Deep learning based web service recommendation methodsJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-22456544:6(9879-9899)Online publication date: 1-Jun-2023
https://dl.acm.org/doi/10.3233/JIFS-224565

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