Mody University International Journal of Computing and Engineering Research

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Efficiency Booster Techniques for Recommendation System


Affiliations
1 Dept. of Computer Science, Mody University of Science and Technology, Sikar, Rajasthan, India
2 Dean, School of Engineering and Technology, Mody University of Science and Technology, Sikar, Rajasthan, India
 

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Recommendation Systems (RS) work as guides, it is guiding users to find products of their interest. It’s a fact that with the increase in RS deployment, traditional methods need modifications. Many techniques and different approaches have been developed to generate an effective recommendation. This is interesting as different application’s scenarios could have a fittest solution. This article throws light on techniques to turn the traditional methods more useful for real-world scenario to boost the productivity of RS. Finally, proposed a similarity fusion method for increasing efficiency of a recommendation system.

Keywords

Algorithm, Efficiency, Recommender Systems, Similarities.
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  • J. Liu, T. Zhou, and B. Wang, “Research process of personalized recommendation system,” Progress in Natural Science, vol. 19, no. 1, pp. 1-15, 2009.

  • J. S. Breese, D. Heckerman, and C. Kadie, “Empirical analysis of predictive algorithms for collaborative filtering,” UAI, 1998.

  • P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl, “Grouplens: An open architecture for collaborative filtering of netnews,” in Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work (CSCW’94), pp. 175-186, Chapel Hill, North Carolina, USA, 22-26 October 1994.

  • J. B. Schafer, J. Konstan, and J. Riedl, “Recommender systems in e-commerce,” in Proceedings of the 1st ACM Conference on Electronic Commerce (EC’99), pp. 158-166, Denver, Colorado, USA, 03-05 November 1999.

  • B. Mobasher, H. Dai, T. Luo, M. Nakagawa, and J. Witshire, “Discovery of aggregate usage profiles for web personalization,” in Proceedings of the WebKDD Workshop, 2000.

  • L. H. Ungar, and D. P. Foster, “Clustering methods for collaborative filtering,” in Workshop on Recommendation Systems at the 15th National Conference on Artificial Intelligence, 1998.

  • R. Burke, “Hybrid recommender systems: Survey and experiments,” User Modeling and User-Adapted Interaction, vol. 12, no. 4, pp. 331-370, 2002.

  • S. Renaud-Deputter, T. Xiong, and S. Wang, ‘‘Combining collaborative filtering and clustering for implicit recommender system,” in 2013 IEEE 27th International Conference on Advanced Information Networking and Applications (AINA), pp. 748-755, 2013.

  • J. Chen, Y. Chen, X. Du, C. Li, J. Lu, S. Zhao, and X. Zhou, “Big data challenge: A data management perspective,” Frontiers of Computer Science, vol. 7, no. 2, pp. 157-164, 2013.

  • M. Jahrer, A. Toscher, and R. Legenstein, “Combining predictions for accurate recommender systems,” in Proceedings of the ACM Conference on Knowledge Discovery in Databases (KDD’2010), 2010.

  • N. Lathia, S. Hailes, L. Capra, and X. Amatriain, “Temporal diversity in recommender systems,” in Proceedings of the SIGIR 2010, Geneva, Switzerland, 19-23 July 2010.

  • Mahout, Apache Software Foundation, 01 June 2018. [Online]. Available: https://mahout.apache.org/. [Accessed 11 September 2018].

  • Y. Li, C. X. Zhai, and Y. Chen, “Exploiting rich user information for one-class collaborative filtering,” Knowledge and Information Systems, vol. 38, no. 2, pp. 277-301, 2014.

  • R. Bambini, P. Cremonesi, and R. Turrin, “A recommender system for an IPTV service provider: A real large-scale production environment,” in F. Ricci et al. (eds.), Recommender Systems Handbook, Springer, New York, NY, 2011.

  • D. Singh, and C. K. Reddy, “A survey on platforms for big data analytics,” Journal of Big Data, Springer open journal, pp. 1-8, 2014.

  • V. D. Blondel, A. Decuyper, and G. Krings, “A survey of results on mobile phone datasets analysis,” EPJ Data Science, pp. 4-10, 2015.

  • G. Miritello, L. Rubén, M. Cebrian, and E. Moro, “Limited communication capacity unveils strategies for human interaction,” Scientific Reports, vol. 3, article no. 1950, 2013.

  • A. Szwabe, M. Ciesielczyk, and T. Janasiewicz, “Semantically enhanced collaborative filtering based on RSVD,” in Proceedings of the International Conference on Computational Collective Intelligence, pp. 10-19, 2011.

  • Y. Guo, and G. Deng, “An improved personalized collaborative filtering algorithm in e-commerce recommender system,” International Conference on Service Systems and Service Management, pp. 1582-1586, IEEE, 2007. Y. Wang, B. Liang, W. Ji, S. Wang, and Y. Chen, “An improved algorithm for personalized recommendation on MOOCs,” International Journal of Crowd Science, vol. 1, no. 3, pp. 186-196, 2017.

  • Y.-A. de Montjoye, Z. Smoreda, R. Trinquart, C. Ziemlicki, and V. D. Blondel, “D4D-Senegal: The second mobile phone data for development challenge,” 2014. ArXiv:1407.4885.

  • M. Deshpande, and G. Karypis, “Item-based top-n recommendation algorithms,” ACM Transactions on Information Systems, vol. 22, no. 1, pp. 143-177, 2004.

  • N. Singh, P. Kumar, and A. K. Dahiya, “RWYW: Recommend what you want - A recommender for mobile plans,” International Journal of Innovations & Advancement in Computer Science, vol. 7, no. 2, pp. 135-144, 2018.


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  • Efficiency Booster Techniques for Recommendation System

Abstract Views: 274  |  PDF Views: 157

Authors

Neetu Singh
Dept. of Computer Science, Mody University of Science and Technology, Sikar, Rajasthan, India
V. K. Jain
Dean, School of Engineering and Technology, Mody University of Science and Technology, Sikar, Rajasthan, India

Abstract


Recommendation Systems (RS) work as guides, it is guiding users to find products of their interest. It’s a fact that with the increase in RS deployment, traditional methods need modifications. Many techniques and different approaches have been developed to generate an effective recommendation. This is interesting as different application’s scenarios could have a fittest solution. This article throws light on techniques to turn the traditional methods more useful for real-world scenario to boost the productivity of RS. Finally, proposed a similarity fusion method for increasing efficiency of a recommendation system.

Keywords


Algorithm, Efficiency, Recommender Systems, Similarities.

References