International Journal of Information and Communication Technology Research
مجله بین المللی ارتباطات و فناوری اطلاعات
International Journal of Information and Communication Technology Research
Engineering & Technology
http://ijict.itrc.ac.ir
1
admin
2251-6107
2783-4425
doi
1652
25391
en
jalali
1393
3
1
gregorian
2014
6
1
6
2
online
1
fulltext
fa
A Cluster-Based Similarity Fusion Approach for Scaling-Up Collaborative Filtering Recommender System
فناوری اطلاعات
Information Technology
پژوهشي
Research
Collaborative Filtering (CF) recommenders work by collecting user ratings for items in a given domain and computing similarities between users or items to produce recommendations. The user-item rating database is extremely sparse. This means the number of ratings obtained is very small compared with the number of ratings that need to be predicted. CF suffers from the sparsity problem, resulting in poor quality recommendations and reduced coverage. Further, a CF algorithm needs calculations that are very expensive and grow non-linearly with the number of users and items in a database. Incited by these challenges, we present Cluster-Based Similarity Fusion (CBSF), a new hybrid collaborative filtering algorithm which can deal with the sparsity and scalability issues simultaneously. By the use of carefully selected clusters of users and items, CBSF reduces the computational cost of traditional CF, while retaining high accuracy. Experimental results demonstrate that apart from being scalable, CBSF leads to a better precision and coverage for the recommendation engine.
recommender systems, collaborative filtering, similarity fusion, clustering
41
52
http://ijict.itrc.ac.ir/browse.php?a_code=A-10-27-103&slc_lang=fa&sid=1
Faezeh Sadat
Gohari
1003194753284600317
1003194753284600317
Yes
Mohammad Jafar
Tarokh
1003194753284600318
1003194753284600318
No