Learning to Rank for Personalised Fashion Recommender Systems via Implicit Feedback

  • Hai Thanh Nguyen
  • Thomas Almenningen
  • Martin Havig
  • Herman Schistad
  • Anders Kofod-Petersen
  • Helge Langseth
  • Heri Ramampiaro
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8891)


Fashion e-commerce is a fast growing area in online shopping. The fashion domain has several interesting properties, which make personalised recommendations more difficult than in more traditional domains. To avoid potential bias when using explicit user ratings, which are also expensive to obtain, this work approaches fashion recommendations by analysing implicit feedback from users in an app. A user’s actual behaviour, such as Clicks, Wants and Purchases, is used to infer her implicit preference score of an item she has interacted with. This score is then blended with information about the item’s price and popularity as well as the recentness of the user’s action wrt. the item. Based on these implicit preference scores, we infer the user’s ranking of other fashion items by applying different recommendation algorithms. Experimental results show that the proposed method outperforms the most popular baseline approach, thus demonstrating its effectiveness and viability.


Recommender System Area Under Curve Implicit Feedback Explicit Feedback Music Recommendation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Hanf, C.H., Wersebe, B.: Price, quality, and consumers’ behaviour. Journal of Consumer Policy 17(3), 335–348 (1994)CrossRefGoogle Scholar
  2. 2.
    Vignali, G., Vignali, C.: Fashion marketing & theory. Access Press (2009)Google Scholar
  3. 3.
    Li, Y., Hu, J., Zhai, C., Chen, Y.: Improving one-class collaborative filtering by incorporating rich user information. In: Proc. of CIKM 2010, pp. 959–968. ACM (2010)Google Scholar
  4. 4.
    Nichols, D.M.: Implicit rating and filtering. In: The DELOS Workshop on Filtering and Collaborative Filtering, pp. 31–36 (1997)Google Scholar
  5. 5.
    Oard, D., Kim, J.: Implicit feedback for recommender systems. In: Proc. of the AAAI Workshop on Recommender Systems, pp. 81–83 (1998)Google Scholar
  6. 6.
    Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: Proc. of IEEE ICDM 2008, pp. 263–272. IEEE CS (2008)Google Scholar
  7. 7.
    Ilievski, I., Roy, S.: Personalized news recommendation based on implicit feedback. In: Proc. of the 2013 International News Recommender Systems Workshop and Challenge, pp. 10–15. ACM (2013)Google Scholar
  8. 8.
    Iwata, T., Watanabe, S., Sawada, H.: Fashion coordinates recommender system using photographs from fashion magazines. In: Proc. of AAAI, pp. 2262–2267 (2011)Google Scholar
  9. 9.
    Shen, E., Lieberman, H., Lam, F.: What am i gonna wear?: Scenario-oriented recommendation. In: Proc. of the 12th International Conference on Intelligent User Interfaces, pp. 365–368. ACM (2007)Google Scholar
  10. 10.
    Yu-Chu, L., Kawakita, Y., Suzuki, E., Ichikawa, H.: Personalized clothing-recommendation system based on a modified bayesian network. In: Proc. of the 12th IEEE/IPSJ Inter. Symp. on Applications and the Internet, pp. 414–417 (2012)Google Scholar
  11. 11.
    Ying Zhao, K.A.: What to wear in different situations? a content-based recommendation system for fashion coordination. In: Proc. of the Japanese Forum on Information Technology (FIT2011) (2011)Google Scholar
  12. 12.
    Liu, S., Feng, J., Song, Z., Zhang, T., Lu, H., Xu, C., Yan, S.: Hi, magic closet, tell me what to wear! In: Proc. of ACM Multimedia 2012, pp. 619–628. ACM (2012)Google Scholar
  13. 13.
    Kao, K.: SuitUp! (2010) Social space centered around clothing items, (Online; accessed August 29, 2014)
  14. 14.
    Xu, S., Jiang, H., Lau, F.C.: Personalized online document, image and video recommendation via commodity eye-tracking. In: Proc. of RecSys, pp. 83–90 (2008)Google Scholar
  15. 15.
    Yang, D., Chen, T., Zhang, W., Lu, Q., Yu, Y.: Local implicit feedback mining for music recommendation. In: Proc. of ACM RecSys 2012, pp. 91–98. ACM (2012)Google Scholar
  16. 16.
    Parra, D., Amatriain, X.: Walk the talk: Analyzing the relation between implicit and explicit feedback for preference elicitation. In: Proc. of the 19th Inter. Conf. on User Modeling, Adaption, and Personalization, pp. 255–268 (2011)Google Scholar
  17. 17.
    Ghosh, P.: From explicit user engagement to implicit product rating (2014), (last accesed August 11, 2014)
  18. 18.
    Jahrer, M., Töscher, A., Legenstein, R.: Combining predictions for accurate recommender systems. In: Proc. of ACM SIGKDD 2010, pp. 693–702. ACM (2010)Google Scholar
  19. 19.
    Shani, G., Gunawardana, A.: Evaluating recommender systems. Technical Report MSR-TR-2009-159, Microsoft Research (2009)Google Scholar
  20. 20.
    Ellen Friedman, T.D.: Practical Machine Learning: Innovations in Recommendation. O’Reilly (2014)Google Scholar
  21. 21.
    Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. In: Proc. of the 25th Conference on Uncertainty in Artificial Intelligence, pp. 452–461. AUAI Press (2009)Google Scholar
  22. 22.
    Apache Software Foundation: Apache Mahout: Scalable machine-learning and data-mining library,
  23. 23.
    Gantner, Z., Rendle, S., Freudenthaler, C., Schmidt-Thieme, L.: MyMediaLite: A free recommender system library. In: Proc. of ACM RecSys 2011. (2011)Google Scholar
  24. 24.
    Bennett, J., Lanning, S.: The Netflix prize. In: KDD Cup and Workshop in conjunction with KDD (2007)Google Scholar
  25. 25.
    Cremonesi, P., Koren, Y., Turrin, R.: Performance of recommender algorithms on top-n recommendation tasks. In: Proceedings of the Fourth ACM Conference on Recommender Systems, RecSys 2010, pp. 39–46 (2010)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Hai Thanh Nguyen
    • 1
  • Thomas Almenningen
    • 2
  • Martin Havig
    • 2
  • Herman Schistad
    • 2
  • Anders Kofod-Petersen
    • 1
    • 2
  • Helge Langseth
    • 2
  • Heri Ramampiaro
    • 2
  1. 1.Telenor ResearchTrondheimNorway
  2. 2.Department of Computer and Information Science (IDI)Norwegian University of Science and Technology (NTNU)TrondheimNorway

Personalised recommendations