Skip to main content
SpringerLink
Log in

Cross-domain recommender system using generalized canonical correlation analysis

  • Regular Paper
  • Published:
Knowledge and Information Systems Aims and scope Submit manuscript

Abstract

Recommender systems provide personalized recommendations to the users from a large number of possible options in online stores. Matrix factorization is a well-known and accurate collaborative filtering approach for recommender system, which suffers from cold-start problem for new users and items. When new users join the system, it will take some time before they enter some ratings in the system, until that time, there are not enough ratings to learn the matrix factorization model. Using auxiliary data such as user’s demographic, ratings and reviews in relevant domains, is an effective solution to reduce the new user problem. In this paper, we used the data of users activity from auxiliary domains to build domain-independent users representation that could be used to predict users ratings in the target domains. We proposed an iterative method which applied MAX-VAR generalized canonical correlation analysis (GCCA) on user’s latent factors learned from matrix factorization on each domain. Also, to improve the capability of GCCA to learn latent factors for new users, we propose a generalized canonical correlation analysis by inverse sum of selection matrices (GCCA-ISSM) approach, which provides better recommendations in cold-start scenarios. The proposed approach is extended using content-based features like topic models extracted from user’s reviews. We demonstrate the accuracy and effectiveness of the proposed approaches on cross-domain rating predictions using comprehensive experiments on Amazon and MovieLens datasets.

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

Access this article

Log in via an institution

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

Similar content being viewed by others

References

  1. Li B, Yang Q, Xue X (2009) Can movies and books collaborate? cross-domain collaborative filtering for sparsity reduction. In: Twenty-first international joint conference on artificial intelligence, 2009

  2. Ohkushi H, Ogawa T, Haseyama M (2010) Kernel CCA-based music recommendation according to human motion robust to temporal expansion. In: 2010 10th international symposium on communications and information technologies. IEEE, pp 1030–1034

  3. Liu C-L, Chen Y-C (2018) Background music recommendation based on latent factors and moods. Knowl Based Syst 159:158–170

    Article  Google Scholar 

  4. Wang H, Zhang F, Xie X, Guo M (2018) Dkn: deep knowledge-aware network for news recommendation. In: Proceedings of the 2018 world wide web conference on world wide web, International World Wide Web Conferences Steering Committee, pp 1835–1844

  5. Singh M (2018) Scalability and sparsity issues in recommender datasets: a survey. Knowl Inf Syst 62:1–43

    Article  Google Scholar 

  6. Park S-T, Chu W (2009) Pairwise preference regression for cold-start recommendation. In: Proceedings of the third ACM conference on Recommender systems. ACM, pp 21–28

  7. Bobadilla J, Ortega F, Hernando A, Gutiérrez A (2013) Recommender systems survey. Knowl Based Syst 46:109–132

    Article  Google Scholar 

  8. Cantador I, Fernández-Tobías I, Berkovsky S, Cremonesi P (2015) Cross-domain recommender systems. In: Ricci F, Rokach L, Shapira B (eds) Recommender systems handbook. Springer, Boston, pp 919–959

    Chapter  Google Scholar 

  9. Sahebi S, Brusilovsky P (2015) It takes two to tango: an exploration of domain pairs for cross-domain collaborative filtering. In: Proceedings of the 9th ACM conference on recommender systems. ACM, pp 131–138

  10. Zhang Q, Wu D, Lu J, Liu F, Zhang G (2017) A cross-domain recommender system with consistent information transfer. Decis Support Syst 104:49–63

    Article  Google Scholar 

  11. Chen L, Zheng J, Gao M, Zhou A, Zeng W, Chen H (2017) Tlrec: transfer learning for cross-domain recommendation. In: 2017 IEEE international conference on big knowledge (ICBK). IEEE, pp 167–172

  12. Ling G, Lyu MR, King I (2014) Ratings meet reviews, a combined approach to recommend. In: Proceedings of the 8th ACM conference on recommender systems. ACM, pp 105–112

  13. Wang D, Liang Y, Xu D, Feng X, Guan R (2018) A content-based recommender system for computer science publications. Knowl Based Syst 157:1–9

    Article  Google Scholar 

  14. Bobadilla J, Hernando A, Ortega F, Gutiérrez A (2012) Collaborative filtering based on significances. Inf Sci 185(1):1–17

    Article  Google Scholar 

  15. Vozalis MG, Margaritis KG (2007) Using SVD and demographic data for the enhancement of generalized collaborative filtering. Inf Sci 177(15):3017–3037

    Article  Google Scholar 

  16. Choi K, Yoo D, Kim G, Suh Y (2012) A hybrid online-product recommendation system: combining implicit rating-based collaborative filtering and sequential pattern analysis. Electron Commer Res Appl 11(4):309–317

    Article  Google Scholar 

  17. Sahu AK, Dwivedi P (2019) User profile as a bridge in cross-domain recommender systems for sparsity reduction. Appl Intell 49:2461–2481

    Article  Google Scholar 

  18. Koren Y, Bell R (2015) Advances in collaborative filtering. In: Recommender systems handbook. Springer, Boston, pp 77–118

  19. Candillier L, Meyer F, Boullé M (2007) Comparing state-of-the-art collaborative filtering systems. In: International workshop on machine learning and data mining in pattern recognition. Springer, pp 548–562

  20. Pazzani MJ (1999) A framework for collaborative, content-based and demographic filtering. Artif intell Rev 13(5–6):393–408

    Article  Google Scholar 

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

    Article  Google Scholar 

  22. Koren Y (2008) Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 426–434

  23. Bell RM, Koren Y (2007) Scalable collaborative filtering with jointly derived neighborhood interpolation weights. In: ICDM, vol 7. Citeseer, pp 43–52

  24. Salakhutdinov R, Mnih A (2008) Bayesian probabilistic matrix factorization using Markov chain Monte Carlo. In: Proceedings of the 25th international conference on machine learning. ACM, pp 880–887

  25. Shan H, Banerjee A (2010) Generalized probabilistic matrix factorizations for collaborative filtering. In: 2010 IEEE international conference on data mining. IEEE, pp 1025–1030

  26. McAuley J, Leskovec J (2013) Hidden factors and hidden topics: understanding rating dimensions with review text. In: Proceedings of the 7th ACM conference on recommender systems. ACM, pp 165–172

  27. Raghavan S, Gunasekar S, Ghosh J (2012) Review quality aware collaborative filtering. In: Proceedings of the sixth ACM conference on recommender systems. ACM, pp 123–130

  28. Musat CC, Liang Y, Faltings B (2013) Recommendation using textual opinions. In: Twenty-third international joint conference on artificial intelligence, 2013

  29. Seroussi Y, Bohnert F, Zukerman I (2011) Personalised rating prediction for new users using latent factor models. In: Proceedings of the 22nd ACM conference on hypertext and hypermedia. ACM, pp 47–56

  30. Chen L, Chen G, Wang F (2015) Recommender systems based on user reviews: the state of the art. User Model User Adapt Interact 25(2):99–154

    Article  Google Scholar 

  31. Berkovsky S, Kuflik T, Ricci F (2007) Cross-domain mediation in collaborative filtering. In: International conference on user modeling. Springer, pp 355–359

  32. Koren Y (2009) Collaborative filtering with temporal dynamics. In: Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 447–456

  33. Pan J, Ma Z, Pang Y, Yuan Y (2013) Robust probabilistic tensor analysis for time-variant collaborative filtering. Neurocomputing 119:139–143

    Article  Google Scholar 

  34. Jiang M, Cui P, Wang F, Yang Q, Zhu W, Yang S (2012) Social recommendation across multiple relational domains. In: Proceedings of the 21st ACM international conference on Information and knowledge management. ACM, pp 1422–1431

  35. Shapira B, Rokach L, Freilikhman S (2013) Facebook single and cross domain data for recommendation systems. User Model User Adapt Interact 23(2–3):211–247

    Article  Google Scholar 

  36. Jawaheer G, Weller P, Kostkova P (2014) Modeling user preferences in recommender systems: a classification framework for explicit and implicit user feedback. ACM Trans Interact Intell Syst (TiiS) 4(2):8

    Google Scholar 

  37. Pan W, Liu Z, Ming Z, Zhong H, Wang X, Xu C (2015) Compressed knowledge transfer via factorization machine for heterogeneous collaborative recommendation. Knowl Based Syst 85:234–244

    Article  Google Scholar 

  38. Gao S, Luo H, Chen D, Li S, Gallinari P, Guo J (2013) Cross-domain recommendation via cluster-level latent factor model. In: Joint European conference on machine learning and knowledge discovery in databases. Springer, pp 161–176

  39. Hwangbo H, Kim Y (2017) An empirical study on the effect of data sparsity and data overlap on cross domain collaborative filtering performance. Expert Syst Appl 89:254–265

    Article  Google Scholar 

  40. Harper FM, Konstan JA (2016) The movielens datasets: history and context. ACM Trans Interact Intell Syst (TIIS) 5(4):19

    Google Scholar 

  41. Singh AP, Gordon GJ (2008) Relational learning via collective matrix factorization. In: Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 650–658

  42. Bouchard G, Yin D, Guo S (2013) Convex collective matrix factorization. In: Artificial intelligence and statistics, pp 144–152

  43. Cortes D (2018) Cold-start recommendations in collective matrix factorization. arXiv preprint arXiv:1809.00366

  44. Faridani S (2011) Using canonical correlation analysis for generalized sentiment analysis, product recommendation and search. In: Proceedings of the fifth ACM conference on recommender systems. ACM, pp 355–358

  45. Bansal T, Belanger D, McCallum A (2016) Ask the GRU: multi-task learning for deep text recommendations. In: Proceedings of the 10th ACM conference on recommender systems. ACM, pp 107–114

  46. Wu H, Zhang Z, Yue K, Zhang B, He J, Sun L (2018) Dual-regularized matrix factorization with deep neural networks for recommender systems. Knowl Based Syst 145:46–58

    Article  Google Scholar 

  47. Catherine R, Cohen W (2017) Transnets: learning to transform for recommendation. In: Proceedings of the eleventh ACM conference on recommender systems. ACM, pp 288–296

  48. Nisha C, Mohan A (2018) A social recommender system using deep architecture and network embedding. Appl Intell 49:1937–1953

    Google Scholar 

  49. Zhang S, Yao L, Sun A, Tay Y (2019) Deep learning based recommender system: a survey and new perspectives. ACM Comput Surv (CSUR) 52(1):5

    Google Scholar 

  50. Hardoon DR, Szedmak S, Shawe-Taylor J (2004) Canonical correlation analysis: an overview with application to learning methods. Neural Comput 16(12):2639–2664

    Article  Google Scholar 

  51. Gower JC, Coppi R, Bolasco S (eds) (1989) Generalized canonical analysis. In: Multiway data analysis. North Holland, Amsterdam, pp 221–232

  52. Carroll JD (1968) Generalization of canonical correlation analysis to three or more sets of variables. In: Proceedings of the 76th annual convention of the American Psychological Association, vol 3, pp 227–228

  53. Van de Velden M, Takane Y (2012) Generalized canonical correlation analysis with missing values. Computational Statistics 27(3):551–571

    Article  MathSciNet  Google Scholar 

  54. Rastogi P, Van Durme B, Arora R (2015) Multiview LSA: representation learning via generalized CCA. In: Proceedings of the 2015 conference of the North American chapter of the Association for Computational Linguistics: human language technologies, pp 556–566

  55. He R, McAuley J (2016) Ups and downs: modeling the visual evolution of fashion trends with one-class collaborative filtering. In: proceedings of the 25th international conference on world wide web. International World Wide Web Conferences Steering Committee, pp 507–517

  56. Bennett J, Lanning S, et al (2007) The Netflix prize. In: Proceedings of KDD cup and workshop, New York, NY, USA, vol 2007, p 35

  57. Asghar N (2016) Yelp dataset challenge: review rating prediction. arXiv preprint arXiv:1605.05362

  58. Wang H, Wang C, Zhai C, Han J (2011) Learning online discussion structures by conditional random fields. In: Proceedings of the 34th international ACM SIGIR conference on research and development in information retrieval. ACM, pp 435–444

  59. Hu L, Cao J, Xu G, Cao L, Gu Z, Zhu C (2013) Personalized recommendation via cross-domain triadic factorization. In: Proceedings of the 22nd international conference on world wide web. ACM, pp 595–606

  60. Loni B, Shi Y, Larson M, Hanjalic A (2014) Cross-domain collaborative filtering with factorization machines. In: European conference on information retrieval. Springer, pp 656–661

  61. Zhang Q, Lu J, Wu D, Zhang G (2018) A cross-domain recommender system with kernel-induced knowledge transfer for overlapping entities. IEEE Trans Neural Netw Learn Syst 30(7):1998–2012

    Article  Google Scholar 

  62. Sahu AK, Dwivedi P (2019) User profile as a bridge in cross-domain recommender systems for sparsity reduction. Appl Intell 49(7):2461–2481

    Article  Google Scholar 

  63. Yan H, Yang C, Yu D, Li Y, Jin D, Chiu D-M (2019) Multi-site user behavior modeling and its application in video recommendation. IEEE Trans Knowl Data Eng. https://doi.org/10.1109/TKDE.2019.2926078

  64. Yu X, Lin J-Y, Jiang F, Du J-W, Han J-Z (2018) A cross-domain collaborative filtering algorithm based on feature construction and locally weighted linear regression. Comput Intell Neurosci 2018:1425365. https://doi.org/10.1155/2018/1425365

  65. Hu G, Zhang Y, Yang Q (2018) Conet: collaborative cross networks for cross-domain recommendation. In: Proceedings of the 27th ACM international conference on information and knowledge management, pp 667–676

  66. Gao C, Chen X, Feng F, Zhao K, He X, Li Y, Jin D (2019) Cross-domain recommendation without sharing user-relevant data. In: The world wide web conference, pp 491–502

  67. Blei DM, Ng AY, Jordan MI (2003) Latent Dirichlet allocation. J Mach Learn Res 3(Jan):993–1022

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Engineering and Information Technology, Amirkabir University of Technology, Tehran, Iran

    Seyed Mohammad Hashemi & Mohammad Rahmati

Authors
  1. Seyed Mohammad Hashemi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohammad Rahmati
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohammad Rahmati.

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

Hashemi, S.M., Rahmati, M. Cross-domain recommender system using generalized canonical correlation analysis. Knowl Inf Syst 62, 4625–4651 (2020). https://doi.org/10.1007/s10115-020-01499-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10115-020-01499-4

Keywords

Search

Navigation