RecLab: a system for eCommerce recommender research with real data, context and feedback
Proceedings of the 2011 Workshop on Context-awareness in Retrieval and Recommendation
A social network-aware top-N recommender system using GPU
Proceedings of the 11th annual international ACM/IEEE joint conference on Digital libraries
A probabilistic definition of item similarity
Proceedings of the fifth ACM conference on Recommender systems
Item recommender system by incorporating metadata information into ternary semantic analysis
Proceedings of the CUBE International Information Technology Conference
Network-Based inference algorithm on hadoop
ISMIS'12 Proceedings of the 20th international conference on Foundations of Intelligent Systems
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Collaborative Filtering(CF) algorithms are widely used in a lot of recommender systems, however, the computational complexity of CF is high thus hinder their use in large scale systems. In this paper, we implement user-based CF algorithm on a cloud computing platform, namely Hadoop, to solve the scalability problem of CF. Experimental results show that a simple method that partition users into groups according to two basic principles, i.e., tidy arrangement of mapper number to overcome the initiation of mapper and partition task equally such that all processors finish task at the same time, can achieve linear speedup.