Title
A Latent Topic Based Collaborative Filtering Recommendation Algorithm for Web Communities
Abstract
Providing personalized high quality community recommendation for Web community members has become increasingly important. Traditional collaborative filtering methods based on explicit topic associations cannot solve the information sparsity problem. The recommendation methods based on latent topic association results in inaccurate results. To solve the above problems, we propose a collaborative Web community recommendation algorithm based on latent topic. Our algorithm generates the latent link between communities and members using latent topic associations to overcome the sparsity problem. Our algorithm also reduces inaccurate results by combining similar members' behaviors and interests. The experiment indicates that our recommendation algorithm has higher recommendation accuracy than traditional methods.
Year
DOI
Venue
2012
10.1109/WISA.2012.41
WISA
Keywords
Field
DocType
inaccurate result,collaborative web community recommendation,latent topic association,collaborative filtering,recommendation algorithm,latent link,recommendation method,recommender systems,latent topic,information sparsity problem,personalized high quality community recommendation,internet,similarity measurement,community recommendation,higher recommendation accuracy,web community member,latent topic based collaborative filtering,high quality community recommendation,recommendation accuracy,collaborative web community recommendation algorithm,explicit topic association,collaborative filtering recommendation algorithm,web communities,collaboration,vectors,filtering,measurement
Recommender system,World Wide Web,Collaborative filtering,Information retrieval,Computer science,Filter (signal processing),Algorithm,Web community,The Internet
Conference
ISBN
Citations 
PageRank 
978-1-4673-3054-1
3
0.40
References 
Authors
11
5
Name
Order
Citations
PageRank
Yu Qian130.74
Zhiyong Peng239583.65
Liang Hong319333.79
Yu Ming4164.82
Jia Dawen530.40