An Enhanced Recommendation Algorithm Based on Modified User-Based Collaborative Filtering

Autor:	Ramil G. Lumauag, Ariel M. Sison, Ruji P. Medina
Rok vydání:	2019
Předmět:	Mean squared error business.industry Computer science Collaborative filtering The Internet Recommendation quality Overfitting Recommender system business Precision and recall Algorithm MovieLens
Zdroj:	ICCCS
DOI:	10.1109/ccoms.2019.8821741
Popis:	With the huge amount of information available on the Internet, recommendation systems gained popularity over the years. Traditional recommendation algorithm usually uses collaborative filtering to determine user and item similarity. However, data sparsity and overfitting affects the accuracy of the recommendation systems that lead to poor recommendation quality. This paper presents an enhanced recommendation algorithm based on modified user-based collaborative filtering to overcome the problem and improve the recommendation quality. The enhanced algorithm was compared to the traditional algorithm using the MovieLens dataset and evaluates its accuracy and performance using the Root Mean Square Error (RSME), Precision and Recall. The experimental results show that the enhanced algorithm outperforms the traditional algorithm and improves the accuracy of the recommendation.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::9dbfcc304343fc1858032c7a25a6ade7 https://doi.org/10.1109/ccoms.2019.8821741 Zobrazit plný text záznamu