Call for Paper - January 2023 Edition
IJCA solicits original research papers for the January 2023 Edition. Last date of manuscript submission is December 20, 2022. Read More

An Improved Recommendation Approach based on User's Standard and Interests

Print
PDF
International Journal of Computer Applications
© 2015 by IJCA Journal
Volume 119 - Number 1
Year of Publication: 2015
Authors:
Rajesh Shukla
Sanjay Silakari
P K Chande
10.5120/21029-2840

Rajesh Shukla, Sanjay Silakari and P K Chande. Article: An Improved Recommendation Approach based on User's Standard and Interests. International Journal of Computer Applications 119(1):6-14, June 2015. Full text available. BibTeX

@article{key:article,
	author = {Rajesh Shukla and Sanjay Silakari and P K Chande},
	title = {Article: An Improved Recommendation Approach based on User's Standard and Interests},
	journal = {International Journal of Computer Applications},
	year = {2015},
	volume = {119},
	number = {1},
	pages = {6-14},
	month = {June},
	note = {Full text available}
}

Abstract

Nowadays, the field of web personalization is growing exponentially. From e-mail, e-trading, and internet forums to social networking based websites, directly or indirectly utilize the concept of web personalization and recommendation system for providing customized services and attention-grabbing offers to their users. By now, a wide range of recommendation systems have been proposed by various researchers and still the research is going on so as to attain the user's expectations. It is a general thought that a user should be recommended the best product from his/her favorite product categories. In this work, we show theoretically that a recommendation system that follows the above criterion does not recommend appropriate products to all types of users. In addition, we propose a user's standard based recommendation system that overcomes this limitation of conventional approach, and provides improved recommendations covering all types of users. For evaluation purpose, we have employed the KDD MovieLens dataset and developed a movie recommendation system based on the proposed approach. We introduce the term 'Gain' for measuring the difference created by the proposed approach as compared to conventional approach. Experimental results reveal that the proposed approach benefited 56% of users and improved 23% of total recommendations.

References

  • A. Vaishnavi, "Effective Web personalization system using Modified Fuzzy Possibilistic C Means," Bonfring International Journal of Software Engineering and Soft Computing, Vol. 1, Special Issue, 2011, pp. 1-7.
  • Anup Prakash Warade , Vignesh Murali Natarajan and Siddharth Sharad Chandak, "How to Develop Online Recommendation Systems that Deliver Superior Business Performance," Cognizant 20-20 Insights.
  • Basu, C. , Hirsh, H. , Cohen W. , "Recommendation as classification: Using Social and Content-Based Information in Recommendation," In Proceedings of the 15th National Conference on Artificial Intelligence, 1998, pp. 714-720.
  • Bin Xu, Jiajun Bu, Chun Chen and Deng Cai, "An Exploration of Improving Collaborative Recommender Systems via User-Item Subgroups," In Proc. of the IEEE 21st international conference on World Wide Web, ACM, 2012, pp. 21-30.
  • Dimitrios Pierrakos, Georgios Paliouras and Yannis Ioannidis, "OurDMOZ: A System for Personalizing the Web," In Proc. of the 6th International Workshop on Personalized Access, Profile Management, and Context Awareness in Databases, 2012.
  • Adobe Test&Target | Adobe Target http://www. adobe. com/products/testandtarget. html
  • Teradata Unveils its Digital Marketing Center: A Complete Hub for True Individualized Marketing. ATLANTA, March 10, 2015 /PRNewswire/
  • Software online Catalog: http://www-142. ibm. com/software/products/us/en/personalized-product-recommen dations/
  • Ilham Esslimani, Armelle Brun, Anne Boyer, "From Social Networks to Behavioral Networks in Recommender Systems," In Proceedings of the International Conference on Advances in Social Network Analysis and Mining, 2009, pp. 143-148.
  • Joseph A. Konstan and John Riedl, "Recommender systems: from algorithms to user experience," Springer, User Modeling and User-Adapted Interaction, Vol. 22, No. 1-2, pp. 101-123.
  • K. Goldberg, T. Roeder, D. Gupta, and C. Perkins Eigentaste, "A constant time collaborative filtering algorithm," Information Retrieval, Vol. 4, Issue. 2, 2001, pp. 133-151.
  • Meenakshi Sharma and Sandeep Mann, "A Survey of Recommender Systems: Approaches and Limitations," International Journal of Innovations in Engineering and Technology, Special Issue, 2013, pp. 1-9.
  • Robin Burke, "Knowledge-based recommender systems,"Encyclopedia of Library and Information Systems," Vol. 69, Supplement 32, New York, 2000.
  • Ronaldo Lima Rocha Campos, Rafaela Lunardi Comarella and Ricardo Azambuja Silveira, "Multiagent Based Recommendation System Model for Indexing and Retrieving Learning Objects," Springer, Communications in Computer and Information Science Vol. 365, 2013, pp. 328-339.
  • Tamas Jambor, Jun Wang and Neal Lathia, "Using Control Theory for Stable and Efficient Recommender Systems," In Proc. of The 21st International Conference on World Wide Web, 2012, pp. 11-20.
  • Vivek Arvind. B Swaminathan. and J Viswanathan. K. R. , "An Improvised Filtering Based Intelligent Recommendation Technique for Web Personalization," In Proc. of Annual IEEE India Conference, 2011, pp. 1194 - 1199.
  • Zi-Ke Zhang, Tao Zhou and Yi-Cheng Zhang "Tag-Aware Recommender Systems: A State-of-the-Art Survey," Springer, Journal of Computer Science and Technology, Vol. 26, No. 5, 2011, pp 767-777.