Advertisement

SNetRS: Social Networking in Recommendation System

  • Jyoti Pareek
  • Maitri Jhaveri
  • Abbas Kapasi
  • Malhar Trivedi
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 177)

Abstract

With the proliferation of electronic commerce and knowledge economy environment both organizations and individuals generate and consume a large amount of online information. With the huge availability of product information on website, many times it becomes difficult for a consumer to locate item he wants to buy. Recommendation Systems [RS] provide a solution to this. Many websites such as YouTube, e-Bay, Amazon have come up with their own versions of Recommendation Systems. However Issues like lack of data, changing data, changing user preferences and unpredictable items are faced by these recommendation systems. In this paper we propose a model of Recommendation systems in e-commerce domain which will address issues of cold start problem and change in user preference problem. Our work proposes a novel recommendation system which incorporates user profile parameters obtained from Social Networking website. Our proposed model SNetRS is a collaborative filtering based algorithm, which focuses on user preferences obtained from FaceBook. We have taken domain of books to illustrate our model.

Keywords

User preferences social networking Recommendation System (RS) Collaborative Filtering (CF) 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bruke, R.: Hybrid Recommender System: Survey and Experiments. User Modeling and User- Adapted Interaction 12(4), 331–370 (2001)CrossRefGoogle Scholar
  2. 2.
    Montaner, M., Lopez, B., de la Rosa, J.L.: A taxonomy of recommender agents on the internet. Artificial Intelligent Review 19(4), 285–330 (2003)CrossRefGoogle Scholar
  3. 3.
    Bogers, A.M.: Recommender Systems for Social Bookmarking ISBN 978-90-8559-582-3Google Scholar
  4. 4.
    Koren, Y., Bell, R.: Advances in Collaborative FilteringGoogle Scholar
  5. 5.
    Oard, D.W., Kim, J.: Implicit Feedback for Recommender Systems. In: Proc. 5th DELOS Workshop on Filtering and Collaborative Filtering, pp. 31–36 (1998)Google Scholar
  6. 6.
  7. 7.
  8. 8.
    Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, pp. 43–52 (1998)Google Scholar
  9. 9.
    Shang, M.S., Zhang, Z.K.: Diffusion-based recommendation in collaborative tagging systems. Chinese Physics Letters 26(11) (2009)Google Scholar
  10. 10.
    Godoy, D., Amandi, A.: Hybrid content and tag-based profiles for recommendation in collaborative tagging systems. In: La-Web (Latin American Web Conference), pp. 58–65 (2008)Google Scholar
  11. 11.
    Kim, J., Kim, H., Ryu, J.H.: TripTip: A trip planning service with tag-based recommendation. Extended Abstracts on Human Factors in Computing Systems, 3467–3472 (2009)Google Scholar
  12. 12.
    Tso, K., Schmidt-Thieme, L.: Attribute-aware collaborative filtering. In: Proceedings of 29th AnnualConference of the German Classification Society (2005)Google Scholar
  13. 13.
    Pazzani, M.J., Billsus, D.: Content-Based Recommendation Systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 325–341. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  14. 14.
    Tso-Sutter, K.H.L., Marinho, L.B., Schmidt-Thieme, L.: Tag-aware recommender systems by fusion of collaborative filtering algorithms. In: Proceedings of the ACM Symposium on Applied Computing, pp. 1995–1999 (2008)Google Scholar
  15. 15.
    Huang, Z., Chen, H., Zeng, D.: Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering. ACM Transactions on Information Systems 22(1), 116–142 (2004)CrossRefGoogle Scholar
  16. 16.
    Liu, J.G., Wang, B.H., Guo, Q.: Improved collaborative filtering algorithm via information transformation. International Journal of Modern Physics C 20(2), 285–293 (2009)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Dey, A.K., Abowd, G.D., Salber, D.: A conceptual framework and a toolkit for supporting the rapid prototyping of context-aware applications. Human-Computer Interaction Journal 16, 97–166 (2001)CrossRefGoogle Scholar
  18. 18.
    Baltrunas, L.: Exploiting contextual information in recommender systems. In: ACM RecSys, pp. 295–298 (2008)Google Scholar
  19. 19.
    Cremonesi, P., Turrin, R.: Analysis of cold-start recommendations in iptv systems. In: Proceedings of the Third ACM Conference on Recommender Systems, pp. 233–236 (October 2009)Google Scholar
  20. 20.
    Yin, H., Chang, G., Wang, X.: A cold-start recommendation algorithm based on new user’s implicit information and multi-attribute rating matrix. In: Proceedings of the Ninth International Confer-ence on Hybrid Intelligent Systems, vol. 2, pp. 353–358 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Jyoti Pareek
    • 1
  • Maitri Jhaveri
    • 2
  • Abbas Kapasi
    • 2
  • Malhar Trivedi
    • 2
  1. 1.Department of Computer ScienceGujarat UniversityAhmedabadIndia
  2. 2.GLS-Institute of Computer TechnologyLaw Garden, Gujarat Technological UniversityAhmedabadIndia

Personalised recommendations