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A Semantic-Based Recommendation Approach for Cold-Start Problem

  • Huynh Thanh-TaiEmail author
  • Nguyen Thai-NgheEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10646)

Abstract

Recommender systems (RS) can predict a list of items which are appropriated to users by using collaborative or content-based filtering methods. The former is more popular than the latter approach, however, it suffers from cold-start problem which can be known as new-user or new-item problems. Since the user/item firstly appears in the system, the RS has no data (feedback) to learn, thus, it cannot provide any recommendation. In this work, we propose using a semantic-based approach to tackle the cold-start problem in recommender systems. With this approach, we create a semantic model to retrieve past similarity data given a new user. Experimental results show that the proposed approach works well for the cold-start problem.

Keywords

Semantic recommendation Recommender systems Cold-start problem New user problem 

References

  1. 1.
    Ahn, H.J.: A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem. Inf. Sci. 178, 37–51 (2008)CrossRefGoogle Scholar
  2. 2.
    Deshpande, M., Karypis, G.: Item-based top-n recommendation algorithms. ACM Trans. Inf. Syst. 22(1), 143–177 (2004)CrossRefGoogle Scholar
  3. 3.
    Lin, J., Sugiyama, K., Kan, M.-Y., Chua, T.-S.: Addressing cold-start in app recommendation: latent user models constructed from twitter followers. In: SIGIR 2013, pp. 283–293 (2013)Google Scholar
  4. 4.
    Bouras, C., Tsogkas, V.: Clustering to deal with the new user problem. In: 2012 IEEE 15th International Conference on Computational Science and Engineering (CSE), pp. 58–65 (2012)Google Scholar
  5. 5.
    Lam, X.N., Vu, T., Le, T.D., Duong, A.D.: Addressing cold-start problem in recommendation systems. In: ICUIMC 2008, pp. 208–211. ACM Press, New York (2008)Google Scholar
  6. 6.
    Nguyen, A., Denos, N., Berrut, C.: Improving new user recommendations with rule-based induction on ColdUser data. In: RecSys 2007, pp. 121–128. ACM Press, New York (2007)Google Scholar
  7. 7.
    Gantner, Z., Drumond, L., Freudenthaler, C., Rendle, S., Schmidt-Thieme, L.: Learning attribute-to-feature mappings for cold-start recommendations. In: Presented at the Proceedings of the 2010 IEEE International Conference on Data Mining (2010)Google Scholar
  8. 8.
    Thai-Nghe, N.: An introduction to factorization technique for building recommendation systems. J. Sci. Univ. Da Lat 6(2013), 44–53 (2013). ISSN 0866-787XGoogle Scholar
  9. 9.
    Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)CrossRefGoogle Scholar
  10. 10.
    Ricci, F., Rokach, L., Shapira, B., Kantor, P.B.: Recommender Systems Handbook, 1st edn. Springer, New York (2010). doi: 10.1007/978-0-387-85820-3 zbMATHGoogle Scholar
  11. 11.
    Son, L.H.: Dealing with the new user cold-start problem in recommender systems: a comparative review. Inf. Syst. 58, 87–104 (2016)CrossRefGoogle Scholar
  12. 12.
    Mairal, J., et al.: Online learning for matrix factorization and sparse coding. J. Mach. Learn. Res. 11, 19–60 (2010)zbMATHMathSciNetGoogle Scholar
  13. 13.
    Thanh-Tai, H., Nguyen, H.-H., Thai-Nghe, N.: A semantic approach in recommender systems. In: Dang, T.K., Wagner, R., Küng, J., Thoai, N., Takizawa, M., Neuhold, E. (eds.) FDSE 2016. LNCS, vol. 10018, pp. 331–343. Springer, Cham (2016). doi: 10.1007/978-3-319-48057-2_23 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Kiengiang UniversityKiengiang CityVietnam
  2. 2.Cantho UniversityCantho CityVietnam

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