Skip to main content

Recommender System Based on Latent Topics

  • 398 Accesses

Part of the Communications in Computer and Information Science book series (CCIS,volume 790)


Collaborative filtering is one of the most used techniques in recommender systems. The goal of this paper is to propose a new method that uses latent topics to model the items to be recommended. In this way, the ability to establish a similarity between these elements is incorporated, improving the performance of the recommendation made. The performance of the proposed method has been measured in two very different contexts, yielding satisfactory results. Finally, the conclusions and some future lines of work are included.


  • Recommender systems
  • Collaborative filtering
  • Latent topic modeling

This is a preview of subscription content, access via your institution.

Buying options

USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-75214-3_17
  • Chapter length: 9 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
USD   74.99
Price excludes VAT (USA)
  • ISBN: 978-3-319-75214-3
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   95.00
Price excludes VAT (USA)
Fig. 1.
Fig. 2.


  1. Adomavicius, G., Tuzhilin, A.: 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 (2005)

    CrossRef  Google Scholar 

  2. Ricci, F., Rokach, L., Shapira, B.: Introduction to recommender systems handbook. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P. (eds.) Recommender Systems Handbook, pp. 1–35. Springer, Boston (2011).

    CrossRef  Google Scholar 

  3. Cheng, X., Yan, X., Lan, Y., Guo, J.: BTM: Topic modeling over short texts. IEEE Trans. Knowl. Data Eng. 26(12), 2928–2941 (2014)

    CrossRef  Google Scholar 

  4. Herlocker, J.L., Konstan, J.A., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 230–237. ACM (1999)

    Google Scholar 

  5. Linden, G., Smith, B., York, J.: recommendations: item-to-item collaborative filtering. IEEE Internet Comput. 7(1), 76–80 (2003)

    CrossRef  Google Scholar 

  6. 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. ACM (2001)

    Google Scholar 

  7. Bell, R.M., Koren, Y.: Scalable collaborative filtering with jointly derived neighborhood interpolation weights. In: 2007 Seventh IEEE International Conference on Data Mining, ICDM 2007, pp. 43–52. IEEE (2007)

    Google Scholar 

  8. Takács, G., Pilászy, I., Németh, B., Tikk, D.: Major components of the gravity recommendation system. ACM SIGKDD Expl. Newslett. 9(2), 80–83 (2007)

    CrossRef  Google Scholar 

  9. California State University: Merlot - multimedia educational resource for learning and online teaching (2017). Accessed 30 June 2017

  10. GroupLens Research: Movielens datasets (2017). Accessed 30 June 2017

  11. Griffiths, T.L., Steyvers, M.: Finding scientific topics. Proc. Nat. Acad. Sci. 101(Suppl 1), 5228–5235 (2004)

    CrossRef  Google Scholar 

  12. Geman, S., Geman, D.: Stochastic relaxation, gibbs distributions, and the bayesian restoration of images. IEEE Trans. Pattern Anal. Mach. Intell. 6, 721–741 (1984)

    CrossRef  MATH  Google Scholar 

  13. Mimno, D., Wallach, H.M., Talley, E., Leenders, M., McCallum, A.: Optimizing semantic coherence in topic models. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 262–272. Association for Computational Linguistics (2011)

    Google Scholar 

  14. Kullback, S., Leibler, R.A.: On information and sufficiency. Ann. Math. Stat. 22(1), 79–86 (1951)

    MathSciNet  CrossRef  MATH  Google Scholar 

  15. Gupta, V., Lehal, G.S.: A survey of common stemming techniques and existing stemmers for Indian languages. J. Emerg. Technol. Web Intell. 5(2), 157–161 (2013)

    Google Scholar 

  16. Lemire, D., Maclachlan, A.: Slope one predictors for online rating-based collaborative filtering. In: Proceedings of the 2005 SIAM International Conference on Data Mining, pp. 471–475. SIAM (2005)

    Google Scholar 

  17. Mnih, A., Salakhutdinov, R.R.: Probabilistic matrix factorization. In: Advances in Neural Information Processing Systems, pp. 1257–1264 (2008)

    Google Scholar 

  18. Koren, Y., Sill, J.: Collaborative filtering on ordinal user feedback. In: IJCAI, pp. 3022–3026 (2013)

    Google Scholar 

  19. Charnelli, M.E., Lanzarini, L., Diaz, J.: Modeling students through analysis of social networks topics. In: XXII Congreso Argentino de Ciencias de la Computacion CACIC 2016, pp. 363–371 (2016)

    Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to María Emilia Charnelli .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Charnelli, M.E., Lanzarini, L., Díaz, J. (2018). Recommender System Based on Latent Topics. In: De Giusti, A. (eds) Computer Science – CACIC 2017. CACIC 2017. Communications in Computer and Information Science, vol 790. Springer, Cham.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-75213-6

  • Online ISBN: 978-3-319-75214-3

  • eBook Packages: Computer ScienceComputer Science (R0)