A Scalable Collaborative Filtering Recommendation Model for Prediction of Movie Rating
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International Journal of Computer Trends and Technology (IJCTT) | 
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| © 2016 by IJCTT Journal | ||
| Volume-42 Number-3 |
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| 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.
Abstract -
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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Keywords
collaborative filtering (cf), locality
sensitive hashing, singular value decomposition,
recommender system.