Using Implicit Preference Relations to Improve Content Based Recommending

  • Ladislav PeskaEmail author
  • Peter Vojtas
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 239)


Our work is generally focused on recommending for small or medium-sized e-commerce portals, where we are facing scarcity of explicit feedback, low user loyalty, short visit times or low number of visited objects. In this paper, we present a novel approach to use specific user behavior as implicit feedback, forming binary relations between objects. Our hypothesis is that if user select some object from the list of displayed objects, it is an expression of his/her binary preference between selected and other shown objects. These relations are expanded based on content-based similarity of objects forming partial ordering of objects. Using these relations, it is possible to alter any list of recommended objects or create one from scratch.

We have conducted several off-line experiments with real user data from a Czech e-commerce site with keyword based VSM and SimCat recommenders. Experiments confirmed competitiveness of our method, however on-line A/B testing should be conducted in the future work.


Content-based recommender system Implicit preference relations VSM User preference E-Commerce 



This work was supported by the grant SVV-2015-260222, P46 and GAUK-126313. The SQL export of the bookshop dataset used during the experiments can be obtained on


  1. 1.
    Baltrunas, L., Amatriain, X.: Towards time-dependant recommendation based on implicit feedback. In: CARS 2009 (RecSys)Google Scholar
  2. 2.
    Claypool, M., Le, P., Wased, M., Brown, D.: Implicit interest indicators. In: IUI 2001, pp. 33–40. ACM (2001)Google Scholar
  3. 3.
    Cremonesi, P., Garzotto, F., Turrin, R.: User-centric vs. system-centric evaluation of recommender systems. In: Kotzé, P., Marsden, G., Lindgaard, G., Wesson, J., Winckler, M. (eds.) INTERACT 2013, Part III. LNCS, vol. 8119, pp. 334–351. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  4. 4.
    Desarkar, M.S., Saxena, R., Sarkar, S.: Preference relation based matrix factorization for recommender systems. In: Masthoff, J., Mobasher, B., Desmarais, M.C., Nkambou, R. (eds.) UMAP 2012. LNCS, vol. 7379, pp. 63–75. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  5. 5.
    Eckhardt, A., Horváth, T., Vojtáš, P.: PHASES: a user profile learning approach for web search. In: WI 2007, pp. 780–783. IEEE (2007)Google Scholar
  6. 6.
    Fang, Y., Si, L.: A latent pairwise preference learning approach for recommendation from implicit feedback. In: CIKM 2012, pp. 2567–2570. ACM (2012)Google Scholar
  7. 7.
    Hidasi, B., Tikk, D.: Initializing matrix factorization methods on implicit feedback databases. J. UCS 19, 1834–1853 (2013)Google Scholar
  8. 8.
    Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: ICDM 2008, pp. 263–272. IEEE (2008)Google Scholar
  9. 9.
    Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Comput. IEEE Comput. Soc. Press 42, 30–37 (2009)CrossRefGoogle Scholar
  10. 10.
    Lai, Y., Xu, X., Yang, Z., Liu, Z.: User interest prediction based on behaviors analysis. Int. J. Digit. Content Technol. Appl. 6(13), 192–204 (2012)CrossRefGoogle Scholar
  11. 11.
    Lee, D.H., Brusilovsky, P.: Reinforcing recommendation using implicit negative feedback. In: Houben, G.-J., McCalla, G., Pianesi, F., Zancanaro, M. (eds.) UMAP 2009. LNCS, vol. 5535, pp. 422–427. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  12. 12.
    Lops, P., de Gemmis, M., Semeraro, G.: Content-based recommender systems: state of the art and trends. In: Recommender Systems Handbook, Springer, Heidelberg, pp. 73–105 (2011)Google Scholar
  13. 13.
    Ostuni, V.C., Di Noia, T., Di Sciascio, E., Mirizzi, R.: Top-N recommendations from implicit feedback leveraging linked open data. In: RecSys 2013, pp. 85–92. ACM (2013)Google Scholar
  14. 14.
    Peska, L.: IPIget– The Component for Collecting Implicit User Preference Indicators. In ITAT 2014, Ustav informatiky AV CR, 2014, 22–26.
  15. 15.
    Peska, L., Vojtas, P.: Evaluating various implicit factors in e-commerce. In: RUE (RecSys) 2014, CEUR, 910, pp. 51–55 (2012)Google Scholar
  16. 16.
    Peska, L., Vojtas, P.: Negative implicit feedback in e-commerce recommender systems. In: WIMS 2013, pp. 45:1–45:4. ACM (2013)Google Scholar
  17. 17.
    Peska, L., Vojtas, P.: Recommending for disloyal customers with low consumption rate. In: Geffert, V., Preneel, B., Rovan, B., Štuller, J., Tjoa, A.M. (eds.) SOFSEM 2014. LNCS, vol. 8327, pp. 455–465. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  18. 18.
    Peska, L., Vojtas, P.: Enhancing recommender system with linked open data. In: Larsen, H.L., Martin-Bautista, M.J., Vila, M.A., Andreasen, T., Christiansen, H. (eds.) FQAS 2013. LNCS, vol. 8132, pp. 483–494. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  19. 19.
    Raman, K., Shivaswamy, P., Joachims, T.: Online learning to diversify from implicit feedback. In: KDD 2012, pp. 705–713. ACM (2012)Google Scholar
  20. 20.
    Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: bayesian personalized ranking from implicit feedback. In: UAI 2009, pp. 452–461. AUAI Press (2009)Google Scholar
  21. 21.
    Yang, B., Lee, S., Park, S., Lee, S.: Exploiting various implicit feedback for collaborative filtering. In: WWW 2012, pp. 639–640. ACM (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Faculty of Mathematics and Physics CharlesUniversity in PraguePragueCzech Republic

Personalised recommendations