Paper Info
Title
A unified latent factor correction scheme for collaborative filtering
Abstract
Collaborative filtering is the most popular technique to ease the information overload issue in the field of recommender system. The nearest neighbor based method and the latent factor based model are two widely used collaborative filtering methods. In order to benefit from both approaches, some researchers have proposed strategies to combine them, and the combinations have been shown to obtain more accurate results, especially during the Netflix competition. However, the unified scheme, which uses the neighborhood information to correct the learnt latent factors, is not well researched. In this paper, we generalize a novel unified scheme by correcting the latent features of users and items with the neighborhood information to boost the recommendations. We further elaborate several state-of-the-art latent factor models and some relationship integrating strategies into the proposed scheme. Finally, we conduct several series of experiments to compare the performance of different methods and latent factor based models within the unified scheme, and conclude with some suggestions in deploying the recommender systems.
Year
DOI
Venue
2014
10.1109/FSKD.2014.6980899
FSKD
Keywords
Field
DocType
latent factor based model,unified latent factor correction scheme,correction shceme,collaborative filtering,pattern recognition,recommender system,latent factors,latent factor model,nearest neighbors,recommender systems,netflix competition,neighborhood information,nearest neighbor based method,information overload issue
Recommender system,Data mining,Collaborative filtering,Pattern recognition,Computer science,Artificial intelligence,Probabilistic latent semantic analysis,Machine learning
Conference
Citations 
PageRank 
References 
1
0.35
13
Authors
5
Name
Order
Citations
PageRank
Penghua Yu172.87
Lanfen Lin27824.70
Ruisong Wang310.35
Jing Wang472.53
Feng Wang5387.16