Designing Distributed Recommender Systems using Map Reduce Paradigm -A Study

Authors

  • Chakraborty PS Dept. of Information Technology, University Institute of Technology, The University of Burdwan, Burdwan, India

Keywords:

Recommender system, Collaborative filtering, Bigdata, Map-reduc, P2P network

Abstract

Nowadays Recommender Systems play an important role in E-Commerce domain. It helps customers to buy the right product or service by generating recommendations. In this paper, a detailed survey has been made regarding the works proposed by different researchers for designing recommender systems in distributed environment. A general framework for designing user-based and item-based recommender systems using map-reduce paradigm has been provide thereafter

References

[1] Shardanand, U. and Maes, P. Social information filtering: algorithms for automating “word of mouth”. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI’95). ACM Press/Addison-Wesley Publishing Co., New York, NY, 210–217,1995.

[2] J. Herlocker, J. A. Konstan, J. Riedl, “Explaining Collaborative Filtering Recommendations”, in Proceedings of ACM Conference on Computer Supported Cooperative Work, Philadelphia, PA, 2000.

[3] Resinck., P., Neophytos, I., Mitesh, S., Peter, B., John, R., 1994. GroupLens: An Open Architecture for Collaborative Filtering of Netnews. Proceedings of the 1994 ACM conference on Computer Supported Cooperative Work, Chapel Hill, North Carolina, United States, p.175-186, 1994.

[4] Stocal, I., et al. (2001). Chord: a scalable peer-to-peer lookup service for Internet applications. In: ACM SIGCOMM, San Diego, CA, USA, pp. 149–160.

[5] A. Tveit, “Peer-to-Peer Based Recommendations for Mobile Commerce”, in Proceedings of the International Workshop on Mobile Commerce, Rome, Italy, 2001.

[6] B. N. Miller, J. A. Konstan, J. Riedl, “PocketLens: Toward a Personal RecommenderSystem”, in ACM Transactions on Information Systems, Vol. 22 (3), 2004.

[7] J. Wang, J. Pouwelse, R. Lagendijk, and M. R. J. Reinders, “Distributed collaborative filtering for peer-to-peer file sharing systems,” in Proceedings of the 21st Annual ACM Symposium on Applied Computing (SAC06), 2006.

[8] T. Oka, H. Morikawa, and T. Aoyama, “Vineyard: A collaborative filtering service platform in distributed environment,” in SAINT-W ’04: Proceedings of the 2004 Symposium on Applications and the Internet- Workshops (SAINT 2004 Workshops). Washington, DC, USA: IEEEComputer Society, 2004, p. 575.

[9] P. Han, B. Xie, F. Yang, and R. Shen, “A scalable P2P recommender system based on distributed collaborative f iltering,” Expert Systems With Applications, vol. 27, no. 2, pp. 203–210, 2004.

[10] P. Liu et al., The Knowledge Grid Based Intelligent Electronic Commerce Recommender Systems, IEEE International Conference on Service-Oriented Computing and Applications (SOCA`07).

[11] F. Yuan et al., A Novel Collaborative Filtering Mechanism for Product Recommendation in P2P Network, Third International IEEE Conference on Signal-Image Technologies and Internet-Based System.

[12] Apache hadoop, http://hadoop.apache.org/.

[13] S. Vinodhini, Building Personalised Recommendation System With Big Data and Hadoop Mapreduce, International Journal of Engineering Research & Technology (IJERT), Vol -3, Issue 4, April - 2014.

[14] D. Valcarce et al., A Distributed Recommendation Platform for Big Data, Journal of Universal Computer Science, vol. 21, no. 13, 2015.

[15] J. Soni, A Hybride Product Recommendation Model Using Hadoop Server for Amazon Dataset, Advances in Computational Sciences and Technology, pp. 1691-1705, Volume 10, Number 6, 2017.

[16] Amazon Dataset: http://jmcauley.ucsd.edu/data/amazon/.

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Published

2025-11-24

How to Cite

[1]
P. Chakraborty, “Designing Distributed Recommender Systems using Map Reduce Paradigm -A Study”, Int. J. Comp. Sci. Eng., vol. 7, no. 1, pp. 289–292, Nov. 2025.