Paper Info
Title
Using A Social-Based Collaborative Filtering With Classification Techniques
Abstract
In this paper, a social-based collaborative filtering model named SBCF is proposed to make personalized recommendations of friends in a social networking context. The social information is formalized and combined with the collaborative filtering algorithm. Furthermore, in order to optimize the performance of the recommendation process, two classification techniques are used: an unsupervised technique applied initially to all users using the Incremental K-means algorithm and a supervised technique applied to newly added users using the K-Nearest Neighbors algorithm (K-NN). Based on the proposed approach, a prototype of a recommender system is developed and a set of experiments has been conducted using the Yelp database.
Year
DOI
Venue
2018
10.1007/978-3-319-89743-1_24
COMPUTATIONAL INTELLIGENCE AND ITS APPLICATIONS
Keywords
Field
DocType
Recommendation of users, Collaborative filtering, Social filtering, Classification, K-means, Incremental K-means, K-nearest neighbors
Recommender system,k-nearest neighbors algorithm,k-means clustering,Collaborative filtering,Social network,Computer science,Artificial intelligence,Social filtering,Social information,Machine learning
Conference
Volume
ISSN
Citations 
522
1868-4238
0
PageRank 
References 
Authors
0.34
7
1
Name
Order
Citations
PageRank
Lamia Berkani1127.44