Editor/Reviewer loginFrequency : 12 issues per year
Subject : Computer Applications and Technology
ISSN : 2319–8656 (Online)
IJCATR Volume 5 Issue 12
A Hybrid Approach for Personalized Recommender System Using Weighted TFIDF on RSS Contents
Rebecca A. Okaka ,Waweru Mwangi, George Okeyo
10.7753/IJCATR0512.1006
keywords : recommender systems; collaborative filtering; content-based filtering; hybrid recommender system; vector space model; term frequency inverse document frequency.
Recommender systems are gaining a great popularity with the emergence of e-commerce and social media on the internet. These recommender systems enable users’ access products or services that they would otherwise not be aware of due to the wealth of information on the internet. Two traditional methods used to develop recommender systems are content-based and collaborative filtering. While both methods have their strengths, they also have weaknesses; such as sparsity, new item and new user problem that leads to poor recommendation quality. Some of these weaknesses can be overcome by combining two or more methods to form a hybrid recommender system. This paper deals with issues related to the design and evaluation of a personalized hybrid recommender system that combines content-based and collaborative filtering methods to improve the precision of recommendation. Experiments done using MovieLens dataset shows the personalized hybrid recommender system outperforms the two traditional methods implemented separately.
@artical{r5122016ijcatr05121006,
Title = "A Hybrid Approach for Personalized Recommender System Using Weighted TFIDF on RSS Contents",
Journal ="International Journal of Computer Applications Technology and Research(IJCATR)",
Volume = "5",
Issue ="12",
Pages ="764 - 774",
Year = "2016",
Authors ="Rebecca A. Okaka ,Waweru Mwangi, George Okeyo"}
This paper proposes a personalized hybrid recommender system.
The hybrid recommender system combines Content-based and collaborative filtering methods.
The Content-based and collaborative filtering methods used the vector space model and tfidf.
The performance of the personalized hybrid recommender system is evaluated