Paper Info
Title
Fuzzy-Bayesian network approach to genre-based recommender systems
Abstract
The World Wide Web has created a new media for mass marketing that can also be highly customized to online customers' needs and expectations. Recommender Systems (RS) play an important role in this area. Here, we aim to establish a genre-based collaborative RS to automatically suggest and rank a list of appropriate items (movies) to a user based on the user profile and the past voting patterns of other users with similar tastes. The contribution of this paper is using genre based information in a hybrid fuzzy-Bayesian network collaborative RS. The interest to the different genres is computed based on a hybrid user model. The similarity of like-minded users according to the fuzzy distance and also Pearson correlation coefficient is involved in a Bayesian network.
Year
DOI
Venue
2010
10.1109/FUZZY.2010.5584250
Fuzzy Systems
Keywords
DocType
ISSN
Bayes methods,Internet,fuzzy set theory,recommender systems,Pearson correlation coefficient,World Wide Web,fuzzy distance,fuzzy-Bayesian network approach,genre-based recommender system,hybrid user model,mass marketing,movies ranking,movies suggestion,past voting patterns,user profile
Conference
1098-7584
ISBN
Citations 
PageRank 
978-1-4244-6919-2
1
0.37
References 
Authors
11
4
Name
Order
Citations
PageRank
Soheila Ashkezari-T.140.78
Mohammad R. Akbarzadeh-Totonchi212518.26
Ashkezari-T, S.310.37
Akbarzadeh-T, M.-R.4476.39