CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Is Always a Hybrid Recommender System Preferable To Single Techniquesh

by Himan Abdollahpouri, Adel Rahmani, Alireza Abdollahpouri
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 82 - Number 4
Year of Publication: 2013
Authors: Himan Abdollahpouri, Adel Rahmani, Alireza Abdollahpouri
10.5120/14108-0361

Himan Abdollahpouri, Adel Rahmani, Alireza Abdollahpouri . Is Always a Hybrid Recommender System Preferable To Single Techniquesh. International Journal of Computer Applications. 82, 4 ( November 2013), 42-48. DOI=10.5120/14108-0361

@article{ 10.5120/14108-0361,
author = { Himan Abdollahpouri, Adel Rahmani, Alireza Abdollahpouri },
title = { Is Always a Hybrid Recommender System Preferable To Single Techniquesh },
journal = { International Journal of Computer Applications },
issue_date = { November 2013 },
volume = { 82 },
number = { 4 },
month = { November },
year = { 2013 },
issn = { 0975-8887 },
pages = { 42-48 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume82/number4/14108-0361/ },
doi = { 10.5120/14108-0361 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:56:56.398662+05:30
%A Himan Abdollahpouri
%A Adel Rahmani
%A Alireza Abdollahpouri
%T Is Always a Hybrid Recommender System Preferable To Single Techniquesh
%J International Journal of Computer Applications
%@ 0975-8887
%V 82
%N 4
%P 42-48
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Collaborative filtering (CF) recommender systems are typically unable to generate adequate recommendations in sparse datasets. Empirical evidence suggests that incorporation of a trust network among the users of a recommender system can significantly help to alleviate this problem. For this reason, some studies have been done on combining CF with trust-enhanced recommender system. In this study, we analyze the switching hybrid recommender system with the CF and trust-enhanced recommender system components from both rating coverage and mean absolute error point of view. Experiments on a dataset from Epinions. com prove that, although the rating coverage of this hybrid method is better than both (CF and trust-enhanced RS), but has lower accuracy than just using trust-enhanced RS. In other words, trust-enhanced RS outperforms the hybrid recommender system consisting of CF and trust-enhanced RS. Finally, we justify this result using analytical method.

References
  1. A. Jøsang, S. Marsh and S. Pope, Exploring different types of trust propagation, LNCS 3986, (2006), 179–192.
  2. A. M. Rashid, G. Karypis and J. Riedl, Influence in ratings based recommender systems: an algorithm-independent approach, in: Proc. of SIAM International Conference on Data Mining, (2005). 57-66.
  3. B. N Miller, J. A Konstan, and J. Riedl, PocketLens: toward a personal recommender system, ACM Trans. Inform. Syst. 22(3), (2002), 437–476.
  4. B. P Knijnenburg, M. C. Willemsen, Z. Gantner, H. Soncu and C. Newell, Explaining the user experience of recommender systems. User Model. User-Adap. Int. 22(4-5) ,(2012), 441-504.
  5. C. Hess, K. Stein and C. Schlieder, Trust-enhanced visibility for personalized document recommendations, in: Proc. of SAC2006, (2006), 1865–1869.
  6. G. Adomavicius and A. Tuzhilin, Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions, IEEE Transactions on Knowledge and Data Engineering. 17(6), (2005), 734–749.
  7. H. J. Ahn, A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem, Information Sciences 178, (2008) 37–51
  8. J. Golbeck, and J. Hendler, FilmTrust: movie recommendations using trust in web-based social networks, in: Proc. of CCNC2006, (2006), 282–286.
  9. J. Golbeck, Computing and applying trust in web-based social networks, PhD thesis (2005).
  10. J. Golbeck, Generating predictive movie recommendations from trust in social networks, LNCS 3986, (2006), 93–104.
  11. J. Herlocker, J. Konstan and J. Riedl, Explaining collaborative filtering recommendation, in: Proc. of CSCW2000, (2000), 241–250
  12. J. Herlocker, J. Konstan, L. Terveen and J. Riedl, Evaluating collaborative filtering recommender systems, ACM Trans. Inform. Syst. 22(1), (2004) 5–53.
  13. J. A. Konstan and J. Riedl, Recommender Systems: From Algorithms to User Experience. User Model. User-Adap. Int. 22(1–2), (2012), 101–123.
  14. L. Zhibing , A trust Enhanced Collaborative filtering recommender System, 5th International Conference on Computer Science and Education (ICCSE), (2010), 384 – 387.
  15. P. Bedi,H. Kaur and S. Marwaha, Trust based recommender system for the semantic web, in: Proc. of IJCAI-07, (2007) 2677–2682.
  16. P. Massa and A. Avesani, Trust-aware collaborative filtering for recommender systems, LNCS 3290, (2004), 492–508.
  17. P. Massa and B. Bhattacharjee, Using trust in recommender systems: an experimental analysis, LNCS 2995, (2004), 221–235.
  18. P. Massa, A. Avesani and R. Tiella, A trust-enhanced recommender system application, Moleskiing, in: Proc. of SAC2005, (2005),1589–1593.
  19. P. Resnick and H. R. Varian, Recommender systems, Communications of the ACM, 40(3), (1997), 56–58.
  20. R. Burke, Hybrid recommender systems: survey and experiments, User Model. User-Adap. Int. 12(4), (2002), 31–370
  21. R. Guha,, R. Kumar, P. Raghavan, and A. Tomkins, Propagation of trust and distrust, in: Proc. of WWW2004, (2004) 403–412.
  22. S. Ray and A. Mahanti, Improving Prediction Accuracy In Trust-Aware Recommender Systems, proceeding of the 43rd Hawaii international conference on systems sciences, 1-9 (2010).
  23. S. E. Middleton, H. Alani, N. R. Shadbolt and D. C. De Roure, Exploiting synergy between ontologies and recommender systems, Proc. of WWW2002 Semantic Web Workshop (2002).
  24. S. T. Park, D. Pennock, O. Madani, N. Good and D. De Coste, Naive ?lterbots for robust cold-start recommendations, Proc. of SIGKDD2006, (2006) , 699-705.
  25. X. Su T. M. Khoshgoftaar, A Survey of Collaborative Filtering Techniques, Advances in Artificial Intelligence, vol. 2009, Article ID 421425, 19 pages, doi:10. 1155/2009/421425.
  26. Z. Huang, H. Chen and D. Zeng, Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering, ACM Trans. Inform. Syst. 22(1), (2004), 116–142
Index Terms

Computer Science
Information Sciences

Keywords

Recommender systems collaborative filtering trust hybrid recommender system

Powered by PhDFocusTM