Hybrid Approach for product Recommendations using Collaborative filtering
DOI:
https://doi.org/10.26438/ijcse/v7i2.564568Keywords:
Collaborative filtering, data mining, recommendation systemAbstract
a successful recommendation approach in data mining can be done with the use of Collaborative Filtering (CF). It deals with the information which is recommended by people. People’s Choice is one of the better aspects of future recommendations. Typically, CF methods are mostly used for solving the problem of data sparsity and cold-start problem. A novel Domain-sensitive Recommendation (DsRec) is an algorithm used for the rating prediction by exploring the user-item subgroup analysis simultaneously. The Proposed work is an extension to DsRec using Trust-based system that considers the trust of the recommender. This type of recommendation system can help to get the information of user’s preferences in different types of domains which make rating predictions trust-worthy and efficient. A trust-based recommendation is complementing the developed algorithm.
References
[1] Jing Liu, Member, Yu Jiang, Zechao Li, Member, Xi Zhang, and Hanqing Lu, Senior Member, IEEE “Domain-Sensitive Recommendation with User-Item Subgroup Analysis”, IEEE Transaction on knowledge and data engineering Vol. 28, NO. 4, April 2016.
[2] N. S. Barley1 & Asst. Prof. R. R. Keole, “A Review of Recommendation System in Domain Sensitive Manner”, Imperial Journal of Interdisciplinary Research (IJIR) Vol-3, Issue-5, 2017 ISSN: 2454-1362.
[3] Bhavya Sanghavi A, Rishabh Rathod A and Dharmeshkumar Mistry, Recommender Systems- “Comparison of Content-based Filtering and Collaborative Filtering”, Oct 2014.
[4] Jaimeel Shah, Lokesh Sahu “A Survey of Various Hybrid based Recommendation Method”, International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Volume 4, Issue 11, November 2014 ISSN: 2277 128X.
[5] J.Ben Schafer, Dan Frankowski, Jon Herlocker, and Shilad Sen “Collaborative Filtering Recommender Systems”. P. Brusilovsky, A. Kobsa, and W. Nejdl (Eds.): The Adaptive Web, LNCS 4321, pp. 291–324, 2007.© Springer-Verlag Berlin Heidelberg 2007.
[6] Y.Zhang, B. Cao, and D.-Y.Yeung, “Multi-domain collaborative filtering”, in Proc. 26th Conf. Annual Conference. Uncertainty Artificial Intelligence , 2010, pp. 725–732.
[7] X. Zhang, J. Cheng, T. Yuan, B. Niu, and H. Lu, “TopRec: Domain specific recommendation through community topic mining in social network”, in Proc. 22nd Int. Conf. World Wide Web, 2013, pp. 1501–1510.
[8] Y. Jiang, J. Liu, X. Zhang, Z. Li, and H. Lu, “TCRec: Product recommendation via exploiting social-trust network and product category information”, in Proc. 22nd Int. Conf. World Wide Web, 2013, pp. 233–234.
[9] Bin XuJiajun Bu Chun Chen Zhejiang Provincial Key Laboratory of Service Robot, College of Computer Science Zhejiang University “An Exploration of Improving Collaborative Recommender Systems via User-Item Subgroups”, April 16–20, 2012.
[10] J. L. Herlocker, J. A. Konstan, A. Borchers, and J. Riedl, “An algorithmic framework for performing collaborative filtering”, in Proc. 22nd Annu. Int. ACM SIGIR Conf. Res. Develop. Inf. Retrieval, 1999, pp. 230–237.
[11] G. Guo, J. Zhang, and D. Thalmann, “A simple but effective method to incorporate trusted neighbors in recommender systems”, in Proceedings of the 20th International Conference on User Modeling, Adaptation and Personalization (UMAP), 2012.
[12] Hao Ma, Haixuan Yang, Michael R. Lyu, Irwin King, “SoRec: Social Recommendation Using Probabilistic Matrix Factorization”, October 26–30, 2008, Napa Valley, California, USA. Copyright 2008 ACM 978-1-59593-991-3/08/10.
[13] Manh Cuong Pham, Yiwei Cao, Ralf Klamma, Matthias Jarke “A Clustering Approach for Collaborative Filtering Recommendation Using Social Network Analysis”, Journal of Universal Computer Science, vol. 17, no. 4 (2011), 583-604 submitted: 30/10/10, accepted: 15/2/11, appeared: 28/2/11 © J.UCS.
[14] Luo Si,Rong Jin, “Flexible Mixture Model for Collaborative Filtering”, Proceeding ICML`03 Proceedings of the Twentieth International Conference on International Conference on Machine Learning Pages 704-711 Washington, DC, USA-August 21-24, 2003 AAAI Press © 2003. ISBN:1-57735-189-4.
[15] M. Jamali and M. Ester, “A matrix factorization technique with trust propagation for recommendation in social networks”, in Proceedings of the 4th ACM Conference on Recommender Systems (RecSys), 2010, pp. 135–142.
[16] T George; S.Meregu “A scalable Collaborative Filtering framework based on Co-clustering”, Data Mining, Fifth IEEE International Conference.27-30 Nov. 2005 IEEE 03 January 2006 ,0-7695-2278-5.
[17] Slobodan Vucetic, Zoran Obradovic “Collaborative Filtering Using A Regression Based Approach, Knowledge And Information Systems”, Center for Information Science and Technology, Temple University, Philadelphia, PA, USA.
[18] Guibing Guo, Jie Zhang, Daniel Thalmann “A Simple but Effective Method to Incorporate Trusted Neighbors in Recommender Systems” Published 2012 in UMAP.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.
