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Unified and Scalable Incremental Recommenders with Consumed Item Packs

  • Rachid Guerraoui
  • Erwan Le MerrerEmail author
  • Rhicheek Patra
  • Jean-Ronan Vigouroux
Conference paper
  • 353 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11725)

Abstract

Recommenders personalize the web content using collaborative filtering to relate users (or items). This work proposes to unify user-based, item-based and neural word embeddings types of recommenders under a single abstraction for their input, we name Consumed Item Packs (CIPs). In addition to genericity, we show this abstraction to be compatible with incremental processing, which is at the core of low latency recommendation to users. We propose three such algorithms using CIPs, analyze them, and describe their implementation and scalability for the Spark platform. We demonstrate that all three provide a recommendation quality that is competitive with three algorithms from the state-of-the-art.

Keywords

Implicit recommenders Incremental updates Parallelism Spark 

References

  1. 1.
    Christakopoulou, E., Karypis, G.: HOSLIM: higher-order sparse linear method for top-n recommender systems. In: PAKDD (2014)Google Scholar
  2. 2.
    Barkan, O., Koenigstein, N.: Item2vec: neural item embedding for collaborative filtering. CoRR abs/1603.04259 (2016)Google Scholar
  3. 3.
    Kabbur, S., Ning, X., Karypis, G.: FISM: factored item similarity models for top-n recommender systems. In: KDD (2013)Google Scholar
  4. 4.
    Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)CrossRefGoogle Scholar
  5. 5.
    Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. CoRR abs/1301.3781 (2013)Google Scholar
  6. 6.
    Lee, T.Q., Park, Y., Park, Y.-T.: An empirical study on effectiveness of temporal information as implicit ratings. Expert. Syst. Appl. 36(2), 1315–1321 (2009)CrossRefGoogle Scholar
  7. 7.
    McAuley, J., Ruining, H.: Fusing similarity models with Markov chains for sparse sequential recommendation. In: ICDM (2016)Google Scholar
  8. 8.
    Boutet, A., Frey, D., Guerraoui, R., Kermarrec, A.-M., Patra, R.: HyRec: leveraging browsers for scalable recommenders. In: Middleware (2014)Google Scholar
  9. 9.
    Bengio, Y., Ducharme, R., Vincent, P., Janvin, C.: A neural probabilistic language model. J. Mach. Learn. Res. 3, 1137–1155 (2003)zbMATHGoogle Scholar
  10. 10.
    Dean, J., et al.: Large scale distributed deep networks. In: NIPS (2012)Google Scholar
  11. 11.
    DeepDist: lightning-fast deep learning on spark. http://deepdist.com/
  12. 12.
    Fontenla-Romero, Ó., Guijarro-Berdiñas, B., Martinez-Rego, D., Pérez-Sánchez, B., Peteiro-Barral, D.: Online machine learning. In: Efficiency and Scalability Methods for Computational Intellect, p. 27 (2013)Google Scholar
  13. 13.
    Chen, C., Yin, H., Yao, J., Cui, B.: TeRec: a temporal recommender system over tweet stream. In: VLDB (2013)Google Scholar
  14. 14.
    CIP-based implicit recommenders: GitHub code repo. https://github.com/rpatra/CIP
  15. 15.
  16. 16.
  17. 17.
  18. 18.
  19. 19.
    Sequence-based recommendations: GitHub code repo. https://github.com/rdevooght/sequence-based-recommendations
  20. 20.
    Guerraoui, R., Le Merrer, E., Patra, R., Vigouroux, J.: Sequences, items and latent links: recommendation with consumed item packs. CoRR abs/1711.06100 (2017)Google Scholar
  21. 21.
    Cremonesi, P., Koren, Y., Turrin, R.: Performance of recommender algorithms on top-n recommendation tasks. In: RecSys (2010)Google Scholar
  22. 22.
    Craswell, N., Szummer, M.: Random walks on the click graph. In: SIGIR (2007)Google Scholar
  23. 23.
    Ostuni, V.C., Di Noia, T., Di Sciascio, E., Mirizzi, R.: Top-n recommendations from implicit feedback leveraging linked open data. In: RecSys (2013)Google Scholar
  24. 24.
    Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: ICDM (2008)Google Scholar
  25. 25.
    Baltrunas, L., Amatriain, X.: Towards time-dependant recommendation based on implicit feedback. In: CARS (2009)Google Scholar
  26. 26.
    Gordea, S., Zanker, M.: Time filtering for better recommendations with small and sparse rating matrices. In: Benatallah, B., Casati, F., Georgakopoulos, D., Bartolini, C., Sadiq, W., Godart, C. (eds.) WISE 2007. LNCS, vol. 4831, pp. 171–183. Springer, Heidelberg (2007).  https://doi.org/10.1007/978-3-540-76993-4_15CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Rachid Guerraoui
    • 1
  • Erwan Le Merrer
    • 2
    Email author
  • Rhicheek Patra
    • 3
  • Jean-Ronan Vigouroux
    • 4
  1. 1.EPFLLausanneSwitzerland
  2. 2.Univ Rennes, Inria, CNRS, IRISARennesFrance
  3. 3.Oracle LabsZurichSwitzerland
  4. 4.TechnicolorRennesFrance

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