PRIN: A Probabilistic Recommender with Item Priors and Neural Models

  • Alfonso LandinEmail author
  • Daniel Valcarce
  • Javier Parapar
  • Álvaro Barreiro
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11437)


In this paper, we present PRIN, a probabilistic collaborative filtering approach for top-N recommendation. Our proposal relies on continuous bag-of-words (CBOW) neural model. This fully connected feedforward network takes as input the item profile and produces as output the conditional probabilities of the users given the item. With that information, our model produces item recommendations through Bayesian inversion. The inversion requires the estimation of item priors. We propose different estimates based on centrality measures on a graph that models user-item interactions. An exhaustive evaluation of this proposal shows that our technique outperforms popular state-of-the-art baselines regarding ranking accuracy while showing good values of diversity and novelty.


Collaborative filtering Neural models Centrality measures 



This work has received financial support from project TIN2015-64282-R (MINECO/ERDF) and accreditation ED431G/01 (Xunta de Galicia/ERDF). The first author acknowledges the support of grant FPU17/03210 (MICIU) and the second author acknowledges the support of grant FPU014/01724 (MICIU).


  1. 1.
    Anthonisse, J.: The rush in a directed graph. Stichting Mathematisch Centrum. Mathematische Besliskunde (BN 9/71), January 1971Google Scholar
  2. 2.
    Balabanović, M., Shoham, Y.: Fab: content-based, collaborative recommendation. Commun. ACM 40(3), 66–72 (1997). Scholar
  3. 3.
    Barkan, O., Koenigstein, N.: Item2vec: neural item embedding for collaborative filtering. In: 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP), pp. 1–6, September 2016.
  4. 4.
    Bavelas, A., Barrett, D.: An Experimental Approach to Organizational Communication. American Management Association (1951)Google Scholar
  5. 5.
    Bellogín, A., Cantador, I., Díez, F., Castells, P., Chavarriaga, E.: An empirical comparison of social, collaborative filtering, and hybrid recommenders. ACM Trans. Intell. Syst. Technol. 4(1), 1–29 (2013). Scholar
  6. 6.
    Bellogín, A., Castells, P., Cantador, I.: Precision-oriented evaluation of recommender systems. In: Proceedings of the 5th ACM Conference on Recommender systems, RecSys 2011, pp. 333–336. ACM, New York (2011).
  7. 7.
    Bellogin, A., Parapar, J.: Using graph partitioning techniques for neighbour selection in user-based collaborative filtering. In: Proceedings of the Sixth ACM Conference on Recommender Systems, RecSys 2012, pp. 213–216. ACM, New York (2012).
  8. 8.
    Boldi, P., Vigna, S.: Axioms for centrality. Internet Math. 10(3–4), 222–262 (2014). Scholar
  9. 9.
    Borgatti, S.P.: Centrality and network flow. Soc. Netw. 27(1), 55–71 (2005). Scholar
  10. 10.
    Cremonesi, P., Koren, Y., Turrin, R.: Performance of recommender algorithms on Top-N recommendation tasks. In: Proceedings of the 4th ACM Conference on Recommender Systems, RecSys 2010, pp. 39–46. ACM, New York (2010).
  11. 11.
    Fleder, D., Hosanagar, K.: Blockbuster culture’s next rise or fall: the impact of recommender systems on sales diversity. Manage. Sci. 55(5), 697–712 (2009). Scholar
  12. 12.
    Freeman, L.C.: A set of measures of centrality based on betweenness. Sociometry 40(1), 35–41 (1977). Scholar
  13. 13.
    de Gemmis, M., Lops, P., Musto, C., Narducci, F., Semeraro, G.: Semantics-aware content-based recommender systems. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 119–159. Springer, Boston (2015). Scholar
  14. 14.
    Grbovic, M., et al.: E-commerce in your inbox: Product recommendations at scale. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2015, pp. 1809–1818. ACM, New York (2015).
  15. 15.
    Guy, I.: Social recommender systems. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 511–543. Springer, Boston, MA (2015). Scholar
  16. 16.
    Harris, Z.S.: Distributional structure. WORD 10(2–3), 146–162 (1954). Scholar
  17. 17.
    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, WWW 2017, pp. 173–182. International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland (2017).
  18. 18.
    Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: Proceedings of the 2008 Eighth IEEE International Conference on Data Mining, ICDM 2008, pp. 263–272. IEEE, Washington (2008).
  19. 19.
    Katz, L.: A new status index derived from sociometric analysis. Psychometrika 18(1), 39–43 (1953). Scholar
  20. 20.
    Kleinberg, J.M.: Authoritative sources in a hyperlinked environment. J. ACM 46(5), 604–632 (1999). Scholar
  21. 21.
    Koren, Y., Bell, R.: Advances in collaborative filtering. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 77–118. Springer, Boston (2015). Scholar
  22. 22.
    Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: Xing, E.P., Jebara, T. (eds.) Proceedings of the 31st International Conference on Machine Learning, pp. 1188–1196. Proceedings of Machine Learning Research, PMLR, Bejing, China, 22–24 June 2014Google Scholar
  23. 23.
    Liang, D., Altosaar, J., Charlin, L., Blei, D.M.: Factorization meets the item embedding: regularizing matrix factorization with item co-occurrence. In: Proceedings of the 10th ACM Conference on Recommender Systems, RecSys 2016, pp. 59–66. ACM, New York (2016).
  24. 24.
    Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient Estimation of Word Representations in Vector Space. CoRR abs/1301.3, January 2013Google Scholar
  25. 25.
    Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems 26, NIPS 2013, pp. 3111–3119. Curran Associates, Inc. (2013)Google Scholar
  26. 26.
    Ning, X., Desrosiers, C., Karypis, G.: A comprehensive survey of neighborhood-based recommendation methods. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 37–76. Springer, Boston (2015). Scholar
  27. 27.
    Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: Bringing order to the web. Technical report, Stanford InfoLab, November 1999Google Scholar
  28. 28.
    Pennington, J., Socher, R., Manning, C.: Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014, pp. 1532–1543. ACL, Stroudsburg (2014).
  29. 29.
    Phaisangittisagul, E.: An Analysis of the regularization between L2 and dropout in single hidden layer neural network. In: Proceedings of the 7th International Conference on Intelligent Systems, Modelling and Simulation, ISMS 2016, pp. 174–179. IEEE (2016).
  30. 30.
    Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, UAI 2009, pp. 452–461. AUAI Press, Arlington (2009)Google Scholar
  31. 31.
    Smucker, M.D., Allan, J., Carterette, B.: A comparison of statistical significance tests for information retrieval evaluation. In: Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management, CIKM 2007, p. 623. ACM, New York (2007).
  32. 32.
    Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014). Scholar
  33. 33.
    Wang, Y., Wang, L., Li, Y., He, D., Chen, W., Liu, T.Y.: A theoretical analysis of NDCG ranking measures. In: Proceedings of the 26th Annual Conference on Learning Theory, COLT 2013, pp. 1–30. (2013)Google Scholar
  34. 34.
    Wasserman, S., Faust, K.: Social Network Analysis: Methods and Applications. Structural Analysis in the Social Sciences. Cambridge University Press, Cambridge (1994)CrossRefGoogle Scholar
  35. 35.
    Zhou, T., Kuscsik, Z., Liu, J.G., Medo, M., Wakeling, J.R., Zhang, Y.C.: Solving the apparent diversity-accuracy dilemma of recommender systems. Proc. Nat. Acad. Sci. 107(10), 4511–4515 (2010). Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Information Retrieval Lab, Department of Computer ScienceUniversity of A CoruñaA CoruñaSpain

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