Recommender System provides various choices of the user preferences for suggesting the product/service to purchase. Collaborative filtering is one of the techniques in Recommender system used to find reviews and ratings of the users for similar products or users. To improve the performance of the recommendation, methods have been sometimes combined in hybrid recommenders. In this paper, the researcher have proposed an item based recommendation using Hybrid method called Item Recommendation Using Hybrid Method (IRHM), based on collaborative filtering approach that recommends the user for choosing the best item. The aim of the paper is to find the maximum value of precision and recall in hybrid method.

Item, Movie, Recommendation System, Hybrid, Collaborative Filtering, IRHM

[1] Saurabh Kumar Tiwari, Shailendra Kumar Shrivastava, “An Approach for Recommender System by Combining Collaborative Filtering with User Demographics and Items Genres”, International Journal of Computer Applications, Vol.128, No.13, pp. 16-24, 2015.
[2] Manoj Kumar, D.K. Yadav, Ankur Singh, Vijay Kr. Gupta, “A Movie Recommender System: MOVREC”, International Journal of Computer Applications, Vol.124, No.3, pp.7-11, 2015.
[3] Prerna Agarwal, Richa Verma, Angshul Majumdar , “Indian Regional Movie Dataset for Recommender Systems”, https://arxiv.org/pdf/1801.02203.pdf, arXiv:1801.02203v1 [cs.IR], pp.1-7, 2018.
[4] Badrul Sarwar, George Karypis, Joseph Konstan, John Riedl, “Analysis of recommendation algorithms for e-commerce”, In the Proceedings of the 2nd ACM Conference on Electronic Commerce, Minnesota, USA, pp.158-167, 2000.
[5] R. Suguna, D. Sharmila, “An Efficient Web Recommendation System using Collaborative Filtering and Pattern Discovery Algorithms”, International Journal of Computer Applications, Vol. 70, No.3, pp.37-44, 2013.
[6] Mohamed Koutheair Khribi, Mohamed Jemnil, Olfa Nasraoui, “Automatic Recommendations for E-Learning Personalization based on Web Usage Mining Techniques and Information Retrieval”, Educational Technology and Society, 12(4), pp.30-42, 2009.
[7] Hamidreza Koohi, Kourosh Kiani, “User based Collaborative Filtering using fuzzy C-means”, Science Direct, Measurement 91, pp.134–139, 2016.
[8] Ziming Zeng, “An Intelligent E-Commerce Recommender System Based on Web Mining”, International Journal Business and Management, Vol.4, No.7, pp.10-14, 2009.
[9] Shivani Diwan, Komal Dani, Sahil Desai, ”Dynamic Recommendation System for E-Commerce Users”, International Research Journal of Engineering and Technology, Vol.03, Issue. 05, pp.141-144, 2016.
[10] Amer Al-Badarenah, Jamal Alsakran, “An Automated Recommender System for Course Selection”, International Journal of Advanced Computer Science and Applications, Vol.7, No.3, pp. 166- 173, 2016.
[11] Mohammad Daoud, S.K. Naqvi, Asad Ahmad, “Opinion Observer:Recommendation System on E-Commerce Website”, International Journal of Computer Applications, Vol.105, No.14, pp.37-42, 2014.
[12] Robin Burke, “Hybrid Recommender Systems: Survey and Experiments”, User Modeling and User - Adapted Interaction”, Vol.12, Issue.4, pp. 331-370, 2002.
[13] Senthil Kumar Thangavel, Neetha Susan Thampi, Johnpaul C I, “Performance Analysis of Various Recommendation Algorithms Using Apache Hadoop and Mahout”, International Journal of Scientific & Engineering Research, Vol.4, Issue 12, pp.279-287, 2013.
[14] Suresh K. Gorakala, Michele Usuelli, “Building a Recommendation System with R”, Packt Publishing Ltd, UK, pp. 9-10, 2015.
[15] Michael Hahsler, “Recommenderlab: A Framework for Developing and Testing Recommendation Algorithms”, Southern Methodist University, pp.1-40, 2011.
[16] Mojtaba Salehi, “An effective recommendation based on user behaviour: a hybrid of sequential pattern of user and attributes of product”, International Journal of Business Information Systems, Vol.14, No.4, pp.480-496, 2013.