Domain Sensitive Recommendation using both User Item Subgroup Analysis and Social Trust Network

Authors(2) :-J. Swarna Latha, A. Sureshbabu

Collaborative Filtering is a conventional technique and a booming Concept in recommendation systems to handle with overload information in the real world. In which the preference of a user on an item is predicted on the strength of the preferences of other users with homogeneous interests. CF attains relationships in between the users and suggests the items to other users. This paper introduces Social matrix factorization, to establish the rating matrix for the user and item. In which everyone convey their ratings on some items beyond creating social connection with other users. Domain clusteringis used for find the domains. The domain clustering model is rendered to make full use of the high- rated users and items are gathering them into groups.

Authors and Affiliations

J. Swarna Latha
M. Tech Scholar, CSE Department, JNTUCEA, Ananthapuramu, Andhra Pradesh, India
A. Sureshbabu
Associate Professor, CSE Department, JNTUCEA, Ananthapuramu, Andhra Pradesh, India

Recommender system, Social trust Network, User-item subgroup.

  1. Jia, D., Zhang, F., & Liu, S. (2013). A Robust Collaborative Filtering Recommendation Algorithm Based on Multidimensional Trust Model. JSW8(1), 11-18.
  2. Jamali, M., & Ester, M. (2010, September). A matrix factorization technique with trust propagation for recommendation in social networks. In Proceedings of the fourth ACM conference on Recommendersystems (pp. 135-142). ACM.
  3. Yuan, W., Guan, D., Lee, Y. K., Lee, S., & Hur, S. J. (2010). Improved trust-aware recommender system using small-worldness of trust networks. Knowledge-Based Systems, 23(3), 232-238.
  4. Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001, April). Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th international conference on World Wide Web (pp. 285-295). ACM.
  5. De Cock, M., & da Silva, P. P. (2005). Propagation of Trust and Distrust. Lecture Notes in Computer Science.
  6. Pham, M. C., Cao, Y., Klamma, R., & Jarke, M. (2011). A  clustering approach for collaborative filtering recommendation using social network analysis. J. UCS17(4), 583-604.
  7. Mnih, A., & Salakhutdinov, R. R. (2008). Probabilistic matrix factorization. In Advances in neural information processing systems (pp.1257-1264).
  8. Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix factorization techniques for recommender systems. Computer, 42(8).

Publication Details

Published in : Volume 3 | Issue 5 | July-August 2017
Date of Publication : 2017-08-31
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 565-569
Manuscript Number : IJSRSET1734131
Publisher : Technoscience Academy

Print ISSN : 2395-1990, Online ISSN : 2394-4099

Cite This Article :

J. Swarna Latha, A. Sureshbabu, " Domain Sensitive Recommendation using both User Item Subgroup Analysis and Social Trust Network, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 3, Issue 5, pp.565-569, July-August-2017.
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