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Transfer Learning via Feature Selection Based Nonnegative Matrix Factorization

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Web Information Systems Engineering – WISE 2019 (WISE 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11881))

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Abstract

Transfer learning has been successfully used in recommender systems to deal with the data sparsity problem. Existing techniques assume that the source and target domains share the same feature space. This paper proposes a new direction in transfer learning where the source and target domains can have different feature space. The proposed technique, Feature Selection based Nonnegative Matrix Factorization (FSNMF), selects the useful features that can minimize the cost function of the target domain. The features of the source domain are learned using NMF and their importance is measured using the gradient principle. Experiments with real-world datasets show the effectiveness of FSNMF in comparison to state-of-the-art relevant transfer learning techniques.

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Notes

  1. 1.

    http://jmcauley.ucsd.edu/data/amazon/.

  2. 2.

    http://www.trustlet.org/downloaded_epinions.html.

  3. 3.

    https://github.com/thirubs/FSNMF.

References

  1. Aggarwal, C.C., et al.: Recommender Systems. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-29659-3

    Book  Google Scholar 

  2. Balasubramaniam, T., Nayak, R., Yuen, C.: Understanding urban spatio-temporal usage patterns using matrix tensor factorization. In: ICDMW, pp. 1497–1498. IEEE (2018)

    Google Scholar 

  3. Berry, M.W., Browne, M., Langville, A.N., Pauca, V.P., Plemmons, R.J.: Algorithms and applications for approximate nonnegative matrix factorization. Comput. Stat. Data Anal. 52(1), 155–173 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  4. Cao, Z., Long, M., Wang, J., Jordan, M.I.: Partial transfer learning with selective adversarial networks. In: CVPR, pp. 2724–2732. IEEE (2018)

    Google Scholar 

  5. Dai, W., Yang, Q., Xue, G.R., Yu, Y.: Boosting for transfer learning. In: ICML, pp. 193–200. ACM (2007)

    Google Scholar 

  6. Fang, Z., Gao, S., Li, B., Li, J., Liao, J.: Cross-domain recommendation via tag matrix transfer. In: ICDMW, pp. 1235–1240. IEEE (2015)

    Google Scholar 

  7. Hao, P., Zhang, G., Martinez, L., Lu, J.: Regularizing knowledge transfer in recommendation with tag-inferred correlation. IEEE Trans. Cybern. 49(1), 83–96 (2017)

    Article  Google Scholar 

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

    Google Scholar 

  9. Hsieh, C.J., Dhillon, I.S.: Fast coordinate descent methods with variable selection for non-negative matrix factorization. In: SIGKDD, pp. 1064–1072. ACM (2011)

    Google Scholar 

  10. Ifada, N., Nayak, R.: Tensor-based item recommendation using probabilistic ranking in social tagging systems. In: WWW, pp. 805–810. ACM (2014)

    Google Scholar 

  11. Jamali, M., Ester, M.: A matrix factorization technique with trust propagation for recommendation in social networks. In: RecSys, pp. 135–142. ACM (2010)

    Google Scholar 

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

    Article  Google Scholar 

  13. Lee, D.D., Seung, H.S.: Algorithms for non-negative matrix factorization. In: Advances in Neural Information Processing Systems, pp. 556–562 (2001)

    Google Scholar 

  14. Long, M., Wang, J., Ding, G., Shen, D., Yang, Q.: Transfer learning with graph co-regularization. IEEE TKDE 26(7), 1805–1818 (2014)

    Google Scholar 

  15. McAuley, J., Targett, C., Shi, Q., Van Den Hengel, A.: Image-based recommendations on styles and substitutes. In: SIGIR, pp. 43–52. ACM (2015)

    Google Scholar 

  16. Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE TKDE 22(10), 1345–1359 (2009)

    Google Scholar 

  17. Pan, W.: A survey of transfer learning for collaborative recommendation with auxiliary data. Neurocomputing 177, 447–453 (2016)

    Article  Google Scholar 

  18. Pan, W., Xiang, E.W., Liu, N.N., Yang, Q.: Transfer learning in collaborative filtering for sparsity reduction. In: AAAI (2010)

    Google Scholar 

  19. Pan, W., Yang, Q.: Transfer learning in heterogeneous collaborative filtering domains. Artif. Intell. 197, 39–55 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  20. Piao, G., Breslin, J.G.: Transfer learning for item recommendations and knowledge graph completion in item related domains via a co-factorization model. In: Gangemi, A., et al. (eds.) ESWC 2018. LNCS, vol. 10843, pp. 496–511. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93417-4_32

    Chapter  Google Scholar 

  21. Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Inf. Process. Manag. 24(5), 513–523 (1988)

    Article  Google Scholar 

  22. Shang, J., Sun, M., Collins-Thompson, K.: Demographic inference via knowledge transfer in cross-domain recommender systems. In: ICDM. IEEE (2018)

    Google Scholar 

  23. Southern, M.: 10% of Twitter users are creating 80% of tweets (2019). https://www.searchenginejournal.com/10-of-twitter-users-are-creating-80-of-tweets/305101/

  24. Takane, Y., Young, F.W., De Leeuw, J.: Nonmetric individual differences multidimensional scaling: an alternating least squares method with optimal scaling features. Psychometrika 42(1), 7–67 (1977)

    Article  MATH  Google Scholar 

  25. Tan, B., Zhang, Y., Pan, S.J., Yang, Q.: Distant domain transfer learning. In: AAAI (2017)

    Google Scholar 

  26. Vavasis, S.A.: On the complexity of nonnegative matrix factorization. SIAM J. Optim. 20(3), 1364–1377 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  27. Verbert, K., et al.: Context-aware recommender systems for learning: a survey and future challenges. IEEE Trans. Learn. Technol. 5(4), 318–335 (2012)

    Article  Google Scholar 

  28. Wibowo, A.T.: Generating pseudotransactions for improving sparse matrix factorization. In: RecSys, pp. 439–442. ACM (2016)

    Google Scholar 

  29. Woolf, M.: A statistical analysis of 1.2 million Amazon reviews (2014). https://minimaxir.com/2014/06/reviewing-reviews/

  30. Xiao, L., Min, Z., Yongfeng, Z., Yiqun, L., Shaoping, M.: Learning and transferring social and item visibilities for personalized recommendation. In: CIKM. ACM (2017)

    Google Scholar 

  31. Xin, X., Liu, Z., Lin, C.Y., Huang, H., Wei, X., Guo, P.: Cross-domain collaborative filtering with review text. In: IJCAI (2015)

    Google Scholar 

  32. Yang, J., Yan, R., Hauptmann, A.G.: Adapting SVM classifiers to data with shifted distributions. In: ICDMW, pp. 69–76. IEEE (2007)

    Google Scholar 

  33. Ying, W., Zhang, Y., Huang, J., Yang, Q.: Transfer learning via learning to transfer. In: ICML, pp. 5072–5081 (2018)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Queensland University of Technology, Brisbane, Australia

    Thirunavukarasu Balasubramaniam & Richi Nayak

  2. Singapore University of Technology and Design, Singapore, Singapore

    Chau Yuen

Authors
  1. Thirunavukarasu Balasubramaniam
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  2. Richi Nayak
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  3. Chau Yuen
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Corresponding author

Correspondence to Thirunavukarasu Balasubramaniam .

Editor information

Editors and Affiliations

  1. University of Hong Kong, Hong Kong SAR, China

    Reynold Cheng

  2. University of Ioannina, Ioannina, Greece

    Nikos Mamoulis

  3. University of California, Los Angeles, CA, USA

    Yizhou Sun

  4. Hong Kong Baptist University, Kowloon Tong, Hong Kong SAR, China

    Xin Huang

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Balasubramaniam, T., Nayak, R., Yuen, C. (2019). Transfer Learning via Feature Selection Based Nonnegative Matrix Factorization. In: Cheng, R., Mamoulis, N., Sun, Y., Huang, X. (eds) Web Information Systems Engineering – WISE 2019. WISE 2020. Lecture Notes in Computer Science(), vol 11881. Springer, Cham. https://doi.org/10.1007/978-3-030-34223-4_6

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  • DOI: https://doi.org/10.1007/978-3-030-34223-4_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-34222-7

  • Online ISBN: 978-3-030-34223-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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