A Scalable Collaborative Filtering Recommendation Model for Prediction of Movie Rating
||International Journal of Computer Trends and Technology (IJCTT)||
|© 2016 by IJCTT Journal|
|Year of Publication : 2016|
|Authors : C. Ugwu, Ogundare, oluwagbenga emmanuel|
|DOI : 10.14445/22312803/IJCTT-V42P125|
C. Ugwu, Ogundare, oluwagbenga emmanuel "A Scalable Collaborative Filtering Recommendation Model for Prediction of Movie Rating". International Journal of Computer Trends and Technology (IJCTT) V42(3):146-154, December 2016. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.
The persistent overwhelming effect on ecommerce users which is as a result of the availability of vast array of product choices demands new techniques of computational intelligence that have the potential of being flexible and producing a better predictive accuracy. The decoupling normalization technique was deployed to correctly represent the true level of user’s interest for several movies in an e-commerce domain. The system used collaborative filtering technique with a hybrid of locality sensitive hashing algorithm and singular value decomposition approach to build the model. Different representative cases of movie ratings were examined from the Movie Lens ratings dataset to validate the model. The system was designed with Object-Oriented Analysis and Design (OO-AD) method and implemented with C-sharp programming language. The results achieved were evaluated with the Mean Average Error (MAE) and Root Square Mean Error (RSME) analysis metrics and the system was found to predict at an accuracy of 90.8%.
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collaborative filtering (cf), locality sensitive hashing, singular value decomposition, recommender system.