A Community Based Reliable Trusted Framework for Collaborative Filtering

Full Text (PDF, 386KB), PP.62-69

Views: 0 Downloads: 0


Satya Keerthi Gorripati 1,* M.Kamala Kumari 2 Anupama Angadi 3

1. Department of CSIT, Gayatri Vidya Parishad College of Engineering, Visakhapatnam, Andhra.Pradesh, India

2. Department of CSE, AdikaviNannaya University, Rajahmundry, Andhra Pradesh, India

3. Department of IT, GMR Institute of Technology, Rajam, Andhra Pradesh, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijisa.2019.02.07

Received: 10 May 2018 / Revised: 2 Jul. 2018 / Accepted: 15 Aug. 2018 / Published: 8 Feb. 2019

Index Terms

Recommender Systems, Reliability, Prediction, Trust Network, Community


Recommender Systems are a primary component of online service providers, formulating plenty of information produced by users’ histories (e.g., their procurements, ratings of products, activities, browsing patterns). Recommendation algorithms use this historical information and their contextual data to offer a list of likely items for each user. Traditional recommender algorithms are built on the similarity between items or users.(e.g., a user may purchase the identical items as his nearest user). In the process of reducing limitations of traditional approaches and to improve the quality of recommender systems, a reliability based community method is introduced.This method comprises of three steps: The first step identifies the trusted relations of the current user by allowing trust propagation in the trust network. In next step, the ratings of selected trusted neighborhood are used for predicting the unrated item of current user. The prediction relies only on items that belong to candidate items’ community. Finally the reliability metric is computed to assess the worth of prediction rating. Experimental results confirmed that the proposed framework attained higher accuracy matched to state-of-the-art recommender system approaches.

Cite This Paper

Satya Keerthi Gorripati, M. Kamala Kumari, Anupama Angadi, "A Community Based Reliable Trusted Framework for Collaborative Filtering", International Journal of Intelligent Systems and Applications(IJISA), Vol.11, No.2, pp.62-69, 2019. DOI:10.5815/ijisa.2019.02.07


[1]Ben Abdrabbah, S., Ayachi, R., Ben Amor, N.: A dynamic community-based personalization for e-Government services. In: Proceedings of the 9th International Conference on Theory and Practice of Electronic Governance, pp. 258–265 (2016).
[2]L. Guo, Q. Peng, A neighbor selection method based on network community detection for collaborative filtering, in: Computer and Information Science (ICIS), 2014 IEEE/ACIS 13th International Conference on, IEEE, 2014, pp. 143-148
[3]Abdrabbah, Sabrine Ben, RaouiaAyachi, and Nahla Ben Amor. "Collaborative Filtering based on Dynamic Community Detection." Dynamic Networks and Knowledge Discovery (2014): 85.
[4]C. Sharma and P. Bedi, CCFRS – Community based Collaborative Filtering Recommender System, Journal of Intelligent & Fuzzy Systems (JIFS) 32 (2017), 2987–2995.
[5]P. Moradi, F. Rezaimehr, S. Ahmadian, M. Jalili, “A trust-aware recommender algorithm based on users overlapping community structure”, International Conference on Advances in ICT for Emerging Regions, IEEE (2016), pp. 162-167.
[6]G. Guo, J. Zhang, D. Thalmann, "Merging trust in collaborative filtering to alleviate data sparsity and cold start", Knowledge Based System. (KBS), vol. 57, pp. 57-68, 2014.
[7]S. K. Sharma and U. Suman, “A trust-based architectural framework for collaborative filtering recommender system”, International Journal of Business Information Systems, Vol. 16, No.2, (2014), pp. 134-153.
[8]J.-C. Ying, B.-N. Shi, V.S. Tseng, H.-W. Tsai, K.H. Cheng, S.-C. Lin, Preference-aware community detection for item recommendation, in: Technologies and Applications of Artificial Intelligence (TAAI), 2013 Conference on, IEEE, 2013, pp. 49-54.
[9]M. Fatemi and L. Tokarchuk, A Community Based Social Recommender System for Individuals & Groups, in International Conference on Social Computing (SocialCom), Washington, DC, USA, 2013, pp. 351–356.
[10]Newman MEJ (2006b) Modularity and community structure in networks. ProcNatlAcadSci 103(23):8577–8582.
[11]J. Xie, M. Chen, and B. K. Szymanski, “LabelrankT: Incremental community detection in dynamic networks via label propagation,” in ACM SIGMOD Workshop on Dynamic Networks Management and Mining (DyNetMM), New York, USA, 2013.
[12]Xie, J., Szymanski, B.K.: LabelRank: a stabilized label propagation algorithm for community detection in networks. CoRR abs/1303.0868 (2013).
[13]JieruiXie , Boleslaw K. Szymanski, Community detection using a neighborhood strength driven Label Propagation Algorithm, Proceedings of the 2011 IEEE Network Science Workshop, p.188-195, June 22-24, 2011 (doi:10.1109/NSW.2011.6004645)
[14]P. Massa and P. Avesani. Trust-aware recommender systems. In ACM Recommender Systems Conference (RecSys), USA, 2007
[15]Gregory, S. 2010. Finding overlapping communities in networks by label propagation. New J. Phys. 12, 10.
[16]P. Moradi and S. Ahmadian, “A Reliability-based Recommendation Method to improve Trust-Aware Recommender Systems,” Journal of Expert Systems with Applications, vol. 42, no. 21, 2015, pp. 109–132.