Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
A Hybrid Representation of Word Images for Keyword Spotting Read chapter Read first chapter

2020 | OriginalPaper | Chapter

Deep Matrix Factorization on Graphs: Application to Collaborative Filtering

Authors : Aanchal Mongia, Vidit Jain, Angshul Majumdar

Published in: Neural Information Processing

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

This work addresses the problem of completing a partially filled matrix incorporating metadata associated with the rows and columns. The basic operation of matrix completion is modeled via deep matrix factorization, and the metadata associations are modeled as graphs. The problem is formally modeled as deep matrix factorization regularized by multiple graph Laplacians. The practical problem of collaborative filtering is an ideal candidate for the proposed solution. It needs to predict missing ratings between users and items, given demographic data of users and metadata associated with items. We show that the proposed solution improves over the state-of-the-art in collaborative filtering.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Correlation-Aware Next Basket Recommendation Using Graph Attention Networks
next chapter Improving Social Recommendations with Item Relationships
Footnotes
Literature
1.
2.
Bobadilla, J., Ortega, F., Hernando, A., Gutiérrez, A.: Recommender systems survey. Knowl.-based Syst. 46, 109–132 (2013)CrossRef
3.
Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. arXiv preprint arXiv:​1301.​7363 (2013)
4.
Candès, E.J., Tao, T.: The power of convex relaxation: near-optimal matrix completion. IEEE Trans. Inf. Theory 56(5), 2053–2080 (2010)MathSciNetCrossRef
5.
Gu, Q., Zhou, J., Ding, C.: Collaborative filtering: weighted nonnegative matrix factorization incorporating user and item graphs. In: Proceedings of the 2010 SIAM International Conference on Data Mining, pp. 199–210. SIAM (2010)
6.
Hofmann, T.: Latent semantic models for collaborative filtering. ACM Trans. Inf. Syst. (TOIS) 22(1), 89–115 (2004)CrossRef
7.
8.
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)CrossRef
9.
Li, T., Ma, Y., Xu, J., Stenger, B., Liu, C., Hirate, Y.: Deep heterogeneous autoencoders for collaborative filtering. In: 2018 IEEE International Conference on Data Mining (ICDM), pp. 1164–1169. IEEE (2018)
10.
Mongia, A., Jhamb, N., Chouzenoux, E., Majumdar, A.: Deep latent factor model for collaborative filtering. Sig. Process. 169, 107366 (2020)CrossRef
11.
Mongia, A., Majumdar, A.: Matrix completion on multiple graphs: application in collaborative filtering. Sig. Process. 165, 144–148 (2019)CrossRef
12.
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web, pp. 285–295 (2001)
13.
Shamir, O., Shalev-Shwartz, S.: Collaborative filtering with the trace norm: learning, bounding, and transducing. In: Proceedings of the 24th Annual Conference on Learning Theory, pp. 661–678 (2011)
14.
Shi, C., et al.: Deep collaborative filtering with multi-aspect information in heterogeneous networks. IEEE Trans. Knowl. Data Eng. (2019)
15.
Shi, X., Yu, P.S.: Limitations of matrix completion via trace norm minimization. ACM SIGKDD Explor. Newsl. 12(2), 16–20 (2011)MathSciNetCrossRef
16.
Sun, Y., Babu, P., Palomar, D.P.: Majorization-minimization algorithms in signal processing, communications, and machine learning. IEEE Trans. Sig. Process. 65(3), 794–816 (2016)MathSciNetCrossRef
17.
Trigeorgis, G., Bousmalis, K., Zafeiriou, S., Schuller, B.W.: A deep matrix factorization method for learning attribute representations. IEEE Trans. Pattern Anal. Mach. Intell. 39(3), 417–429 (2016)CrossRef
18.
Wang, J., De Vries, A.P., Reinders, M.J.: Unifying user-based and item-based collaborative filtering approaches by similarity fusion. In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 501–508 (2006)
Metadata
Title
Deep Matrix Factorization on Graphs: Application to Collaborative Filtering
Authors
Aanchal Mongia
Vidit Jain
Angshul Majumdar
Copyright Year
2020
Book
Neural Information Processing

Print ISBN: 978-3-030-63819-1

Electronic ISBN: 978-3-030-63820-7

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-63820-7_86

Premium Partner

    Image Credits