Skip to main content

Advertisement

Springer Nature Link
Log in

A General Matrix Factorization Framework for Recommender Systems in Multi-access Edge Computing Network

  • Published:
Mobile Networks and Applications Aims and scope Submit manuscript

Abstract

Due to the growing number of users and items in recommender system, along with the more complex algorithms for precise recommendation, recommender system in broswer/server architecture will consume more computing cost and more service latency. Besides, the bidirectional transmission of broswer/server architecture requires users to upload necessary user information to the cloud servers. The current manner of recommender systems might cause the leakage of the sensitive information and take the risk of privacy issue. To alleviate the two issues, we integrate the multi-access edge computing network into recommender systems to take fully advantage of base stations and user terminals. Particularlly, we pull the tasks of user-profiles and recommendation algorithms out of the cloud servers and put them to base stations and user terminals. The cloud servers will undertake an independent item-profiles task that will not take any information from users. Considering the differences among user terminals, we propose a general matrix factorization framework that can adopt different matrix factorization-based recommender algorithms with one item-profiles. This framework can allow different terminals to take variety algorithms based on their computing abilities. Experiments are conducted on two real world datasets to validate the proposed methods by comparing them with conventional recommendation methods. Experimental results prove the principle that matrix factorization methods within the proposed framework can enhance the recommendation system’s performance in terms of both prediction and recommendation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

References

  1. Adomavicius G, Tuzhilin A (2005) Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans Knowl Data Eng 17(6):734–749

    Article  Google Scholar 

  2. Ricci F, Rokach L, Shapira B (2015) Recommender systems: Introduction and challenges. In: recommender systems handbook. Springer, Boston, pp 1–34

  3. Seo Y, Kim Y, Lee E, Kim H (2021) Group recommender system based on genre preference focusing on reducing the clustering cost. Expert Syst Appl:183

  4. Bag S, Ghadge A, Tiwari MK (2019) An integrated recommender system for improved accuracy and aggregate diversity. Comput Ind Eng 130:187–197

    Article  Google Scholar 

  5. Liu D, Yang C (2018) A Learning-Based approach to joint content caching and recommendation at base stations. In: Proceedings of IEEE Global Communications Conference, pp 1–7

  6. Yang J, Wang H, Lv Z, Wei W, Song H, Kantarci ME, Kantarci B, He S (2017) Multimedia recommendation and transmission system based on cloud platform. Futur Gener Comput Syst 70:94–103

    Article  Google Scholar 

  7. Fu Y, Yang Z, Quek TQS, Yang HH (2021) Towards cost minimization for wireless caching networks with recommendation and uncharted users’ feature information. IEEE transactions on wireless communications (early access)

  8. Zhou X, Canady R, Bao S, Gokhale A (2020) Cost-effective Hardware Accelerator Recommendation for Edge Computing. In: Proceedings of 3rd USENIX Workshop on Hot Topics in Edge Computing

  9. Su X, Sperlì G, Moscato V, Picariello A, Esposito C, Choi C (2019) An edge intelligence empowered recommender system enabling cultural heritage applications. IEEE Trans Ind Inf 15(7):4266–4275

    Article  Google Scholar 

  10. Gong Y, Jiang Z, Feng Y, Hu B, Zhao K, Liu Q, Ou W (2020) Edgerec: Recommender System on Edge in Mobile Taobao. In: Proceedings of 29th ACM International Conference on Information and Knowledge Management, pp 2477–2484

  11. Gao J, Wang X, Wang Y, Xie X (2019) Explainable recommendation through attentive Multi-View learning. In: Proceedings of 33rd AAAI Conference on Artificial Intelligence, pp 3622– 3629

  12. Baltrunas L, Ludwig B, Ricci F (2011) Matrix factorization techniques for context aware recommendation. In: Proceedings of 5th ACM Conference on Recommender System, pp 301–304

  13. Zhao WX, Li S, He Y, Chang EY, Wen J, Li X (2016) Connecting social media to E-Commerce: Cold-start product recommendation using microblogging information. IEEE Trans Knowl Data Eng 28 (5):1147–1159

    Article  Google Scholar 

  14. Casino F, Domingo-Ferrer J, Patsakis C, Puig D, Solanas A (2005) A k-anonymous approach to privacy preserving collaborative filtering. J Comput Syst Sci 81(6):1000–1011

  15. Zhu J, He P, Zheng Z, Lyu MR (2005) A Privacy-Preserving QoS Prediction Framework for Web Service Recommendation. In: Proceedings of IEEE International Conference on Web Services, pp 241–248

  16. Kuang L, Tu S, Zhang Y (2020) X, Yang Providing privacy preserving in next POI recommendation for Mobile edge computing. J Cloud Comput 9(10

  17. Qi L, Wang R, Hu C, Li S, He Q, Xu X (2019) Time-aware distributed service recommendation with privacy-preservation. Inf Sci 480:354–364

    Article  Google Scholar 

  18. Zhong W, Yin X, Zhang X, Li S, Dou W, Wang R, Qi L (2020) Multi-dimensional quality-driven service recommendation with privacy-preservation in mobile edge environment. Comput Commun 157:116–123

    Article  Google Scholar 

  19. Sun C, Hui L, Li X, Wen J, Xiong Q, Wang X, Leun VCM (2020) Task offloading for End-Edge-Cloud orchestrated computing in mobile networks. In: Proceedings of IEEE Wireless Communications and Networking Conference, pp 1–6

  20. Sun C, Li H, Li X, Wen J, Xiong Q, Zhou W (2020) Convergence of recommender systems and edge computing: a comprehensive survey. IEEE Access 8:47118–47132

    Article  Google Scholar 

  21. Luo X, Zhou M, Li S, You Z, Xia Y, Zhu Q (2016) A nonnegative latent factor model for Large-Scale sparse matrices in recommender systems via alternating direction method. IEEE Trans Neural Netw Learn Syste 23(3):579–592

    Article  MathSciNet  Google Scholar 

  22. Koren Y, Bell R, Volinsky C (2009) Matrix factorization techniques for recommender systems. Computer 42(8):30– 37

    Article  Google Scholar 

  23. Lee DD, Seung HS (2000) Algorithms for Non-negative Matrix Factorization. In: Proceedings of International Conference on Neural Information Processing Systems, pp 556–562

  24. Mnih A, Salakhutdinov RR (2007) Probabilistic matrix factorization. In: Proceedings of International Conference on Neural Information Processing Systems, pp 1257–1264

  25. Harper FM, Konstan JA (2015) The MovieLens Datasets: History and Context. ACM Trans Interact Intell Syst 5(4):19:1–19:19

  26. Zhang Y, Zhang M, Zhang Y, Lai G, Liu Y, Zhang H, Ma S (2005) Daily-aware Personalized Recommendation based on Feature-Level Time Series Analysis. In: Proceedings of the 24th International Conference Companion on World Wide Web, pp 1373–1383

  27. Pauca VP, Piper J, Plemmons RJ (2006) Nonnegative matrix factorization for spectral data analysis. Linear Algebra Appl 416(1):29–47

    Article  MathSciNet  Google Scholar 

  28. Pan SJ, Yang Q (2010) A survey on transfer learning. IEEE Trans Knowl Data Eng 22 (10):1345–1359

    Article  Google Scholar 

  29. Xue H, Dai X, Zhang J, Huang S, Chen J (2017) Deep matrix factorization models for recommender systems. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence, pp 3203–3209

  30. Hornik K (1991) Approximation capabilities of multilayer feedforward networks. Neural Netw 4 (2):251–257

    Article  MathSciNet  Google Scholar 

  31. He X, Liao L, Zhang H, Nie L, Hu X, Chua T (2017) Neural collaborative filtering. In: Proceedings of the 26th International Conference on World Wide Web, pp 173–182

  32. Rendle S, Krichene W, Zhang L, Anderson J (2020) Neural Collaborative Filtering vs. Matrix Factorization Revisited. In: Proceedings of the 14th ACM Conference on Recommender Systems, pp 240–248

  33. Kim D, Park C, Oh J, Lee S, Yu H (2016) Convolutional matrix factorization for document Context-Aware recommendation. In: Proceedings of the 10th ACM Conference on Recommender Systems, pp 233–240

  34. Zhang S, Yao L, Xu X (2017) AutoSVD++: An Efficient Hybrid Collaborative Filtering Model via Contractive Auto-encoders. In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp 957–960

  35. Ortega F, Lara-Cabrera R, González-Prieto A, Bobadilla J (2021) Providing reliability in recommender systems through Bernoulli Matrix Factorization. Inf Sci 553:110–128

    Article  MathSciNet  Google Scholar 

  36. He X, Zhang H, Kan M, Chua T (2016) Fast matrix factorization for online recommendation with implicit feedback. In: Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, pp 549–558

  37. Wu Y, DuBois C, Zheng AX, Ester M (2016) Collaborative denoising Auto-Encoders for Top-N recommender systems. In: Proceedings of the 9th ACM International Conference on Web Search and Data Mining, pp 153–162

  38. Chen J, Wang C, Zhou S, Shi Q, Chen J, Feng Y, Chen C (2020) Fast adaptively weighted matrix factorization for recommendation with implicit feedback. In: Proceedings of 34th AAAI Conference on Artificial Intelligence, pp 3470–3477

Download references

Acknowledgements

This research is supported by the National Science Foundations of China under grant No. 62072060, the Natural Science Foundation Projects in Chongqing under grant No. cstc2019jcyj-msxmX0442 and the Graduate Research and Innovation Foundation of Chongqing under grant No. CYB21068.

Author information

Authors and Affiliations

  1. School of Big Data, Software Engineering, Chongqing University, Chongqing, China

    Guanzhong Liang, Chuan Sun, Jianing Zhou, Junhao Wen & Xiuhua Li

  2. University of Sydney, Sydney, Australia

    Fengji Luo

Authors
  1. Guanzhong Liang
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Chuan Sun
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Jianing Zhou
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Fengji Luo
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Junhao Wen
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Xiuhua Li
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Guanzhong Liang.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liang, G., Sun, C., Zhou, J. et al. A General Matrix Factorization Framework for Recommender Systems in Multi-access Edge Computing Network. Mobile Netw Appl 27, 1629–1641 (2022). https://doi.org/10.1007/s11036-021-01869-4

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11036-021-01869-4

Keywords