(Publisher of Peer Reviewed Open Access Journals)

International Journal of Advanced Technology and Engineering Exploration (IJATEE)

ISSN (Print):2394-5443    ISSN (Online):2394-7454
Volume-6 Issue-55 June-2019
Full-Text PDF
Paper Title : Improved CF based prediction technique for recommendation systems
Author Name : Ruchita Sharma and Manish Sharma
Abstract :

Any eCommerce websites use recommendation system to recommend items to users. Collaborative filtering is a technique to recommend an item to the customers by understanding the past behavior of the same user and other similar users. The accuracy of the recommendation system is a major issue while recommending items to users. In this paper a research is done to propose a new system to predict ratings for items for the users by finding similarity between the users. Similarity between the users is found by analyzing the previous history of the users for rating items. A similarity matrix is created that store a similar weight between users. Similar users are selected if the similarity weight between the users is found greater than a similarity threshold. The proposed system is implemented on a data set and the quality of the proposed system is analyzed by comparing the value of mean absolute error (MAE). The experimental results are found better than some other existing techniques. The value of MAE is approximate 11% better and value of RMSE is 15% better as compared to existing algorithms.

Keywords : Recommendation systems, Collaborative filtering, Prediction system.
Cite this article : Sharma R, Sharma M. Improved CF based prediction technique for recommendation systems. International Journal of Advanced Technology and Engineering Exploration. 2019; 6(55):180-185. DOI:10.19101/IJATEE.2019.650038.
References :
[1]Mohamed H, Abdulsalam L, Mohammed H. Adaptive genetic algorithm for improving prediction accuracy of a multi-criteria recommender system. In international symposium on embedded multicore/many-core systems-on-chip 2018 (pp. 79-86). IEEE.
[Crossref] [Google Scholar]
[2]Hassan M, Hamada M. Performance analysis of neural networks-based multi-criteria recommender systems. In international conferences on information technology, information systems and electrical engineering 2017 (pp. 490-4). IEEE.
[Crossref] [Google Scholar]
[3]Ze W, Dengwen Z. Optimization collaborative filtering recommendation algorithm based on ratings consistent. In international conference on software engineering and service science 2016 (pp. 1055-8). IEEE.
[Crossref] [Google Scholar]
[4]Rodrigues CM, Rathi S, Patil G. An efficient system using item & user-based CF techniques to improve recommendation. In international conference on next generation computing technologies 2016 (pp. 569-74). IEEE.
[Crossref] [Google Scholar]
[5]Ying Y, Cao Y. Collaborative filtering recommendation combining FCM and slope one algorithm. In international conference on informative and cybernetics for computational social systems 2015 (pp. 110-5). IEEE.
[Crossref] [Google Scholar]
[6]Gupta J, Gadge J. Performance analysis of recommendation system based on collaborative filtering and demographics. In international conference on communication, information & computing technology 2015 (pp. 1-6). IEEE.
[Crossref] [Google Scholar]
[7]Wei S, Ye N, Zhang S, Huang X, Zhu J. Collaborative filtering recommendation algorithm based on item clustering and global similarity. In international conference on business intelligence and financial engineering 2012 (pp. 69-72). IEEE.
[Crossref] [Google Scholar]
[8]Shambour Q, Hourani M, Fraihat S. An item-based multi-criteria collaborative filtering algorithm for personalized recommender systems. International Journal of Advanced Computer Science and Applications. 2016; 7(8):274-9.
[Google Scholar]
[9]Hassan M, Hamada M. A neural networks approach for improving the accuracy of multi-criteria recommender systems. Applied Sciences. 2017; 7(9):1-18.
[Crossref] [Google Scholar]
[10]Adomavicius G, Kwon Y. New recommendation techniques for multicriteria rating systems. IEEE Intelligent Systems. 2007; 22(3):48-55.
[Crossref] [Google Scholar]
[11]Konstan JA, Riedl J. Recommender systems: from algorithms to user experience. User Modeling and User-Adapted Interaction. 2012; 22(1-2):101-23.
[Crossref] [Google Scholar]
[12]Zhu X, Ye H, Gong S. A personalized recommendation system combining case-based reasoning and user-based collaborative filtering. In Chinese control and decision conference 2009 (pp. 4026-8). IEEE.
[Crossref] [Google Scholar]
[13]Zarzour H, Al-Sharif Z, Al-Ayyoub M, Jararweh Y. A new collaborative filtering recommendation algorithm based on dimensionality reduction and clustering techniques. In international conference on information and communication systems 2018 (pp. 102-6). IEEE.
[Crossref] [Google Scholar]
[14]Zhang H, Ganchev I, Nikolov NS, ODroma M. A trust-enriched approach for item-based collaborative filtering recommendations. In international conference on intelligent computer communication and processing (ICCP) 2016 (pp. 65-8). IEEE.
[Crossref] [Google Scholar]
[15]https://grouplens.org/datasets/movielens/. Accessed 12 March 2019.