Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
Recommending and evaluating choices in a virtual community of use
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Communications of the ACM
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Inferring Web communities from link topology
Proceedings of the ninth ACM conference on Hypertext and hypermedia : links, objects, time and space---structure in hypermedia systems: links, objects, time and space---structure in hypermedia systems
Horting hatches an egg: a new graph-theoretic approach to collaborative filtering
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Finding related pages in the World Wide Web
WWW '99 Proceedings of the eighth international conference on World Wide Web
Trawling the Web for emerging cyber-communities
WWW '99 Proceedings of the eighth international conference on World Wide Web
Authoritative sources in a hyperlinked environment
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
Recommender systems in e-commerce
Proceedings of the 1st ACM conference on Electronic commerce
Efficient identification of Web communities
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Creating a Web community chart for navigating related communities
Proceedings of the 12th ACM conference on Hypertext and Hypermedia
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
An Approach to Relate the Web Communities through Bipartite Graphs
WISE '01 Proceedings of the Second International Conference on Web Information Systems Engineering (WISE'01) Volume 1 - Volume 1
Finding a Maximum Density Subgraph
Finding a Maximum Density Subgraph
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
A method of extracting malicious expressions in bulletin board systems by using context analysis
Information Processing and Management: an International Journal
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part II
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
RoClust: Role discovery for graph clustering
Web Intelligence and Agent Systems
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In E-commerce sites, recommendation systems are used to recommend products to their customers. Collaborative filtering (CF) is widely employed approach to recommend products. In the literature, researchers are making efforts to improve the scalability and online performance of CF. In this paper we propose a graph based approach to improve the performance of CF. We abstract a neighborhood community of a given customer through dense bipartite graph (DBG). Given a data set of customer preferences, a group of neighborhood customers for a given customer is extracted by extracting corresponding DBG. The experimental results on the MovieLens data set show that the recommendation made with the proposed approach matches closely with the recommendation of CF. The proposed approach possesses a potential to adopt to frequent changes in the product preference data set.