Recommender System Based on Latent Topics

  • María Emilia CharnelliEmail author
  • Laura Lanzarini
  • Javier Díaz
Conference paper
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 


  1. 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)CrossRefGoogle Scholar
  2. 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). CrossRefGoogle Scholar
  3. 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)CrossRefGoogle Scholar
  4. 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. 5.
    Linden, G., Smith, B., York, J.: recommendations: item-to-item collaborative filtering. IEEE Internet Comput. 7(1), 76–80 (2003)CrossRefGoogle Scholar
  6. 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. 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. 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)CrossRefGoogle Scholar
  9. 9.
    California State University: Merlot - multimedia educational resource for learning and online teaching (2017). Accessed 30 June 2017
  10. 10.
    GroupLens Research: Movielens datasets (2017). Accessed 30 June 2017
  11. 11.
    Griffiths, T.L., Steyvers, M.: Finding scientific topics. Proc. Nat. Acad. Sci. 101(Suppl 1), 5228–5235 (2004)CrossRefGoogle Scholar
  12. 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)CrossRefzbMATHGoogle Scholar
  13. 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. 14.
    Kullback, S., Leibler, R.A.: On information and sufficiency. Ann. Math. Stat. 22(1), 79–86 (1951)MathSciNetCrossRefzbMATHGoogle Scholar
  15. 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. 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. 17.
    Mnih, A., Salakhutdinov, R.R.: Probabilistic matrix factorization. In: Advances in Neural Information Processing Systems, pp. 1257–1264 (2008)Google Scholar
  18. 18.
    Koren, Y., Sill, J.: Collaborative filtering on ordinal user feedback. In: IJCAI, pp. 3022–3026 (2013)Google Scholar
  19. 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

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.LINTI - Research Laboratory in New Information TechnologiesNational University of La PlataLa PlataArgentina
  2. 2.III LIDI - Computer Science Research Institute LIDI Computer Science SchoolNational University of La PlataLa PlataArgentina
  3. 3.CONICET - National Scientific and Technical Research CouncilBuenos AiresArgentina

Personalised recommendations