An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
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
Methods and metrics for cold-start recommendations
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Learning Collaborative Information Filters
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
ACM Transactions on Information Systems (TOIS)
Information Diffusion Approach to Cold-Start Problem
WI-IATW '07 Proceedings of the 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops
Lessons from the Netflix prize challenge
ACM SIGKDD Explorations Newsletter - Special issue on visual analytics
A random walk method for alleviating the sparsity problem in collaborative filtering
Proceedings of the 2008 ACM conference on Recommender systems
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
Alleviating the sparsity problem of collaborative filtering using trust inferences
iTrust'05 Proceedings of the Third international conference on Trust Management
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Since the late 20th century, the number of Internet users has noticeably increased. Recently, the number of Internet queries and the quantity of information available on the web has increased drastically. A large amount of new information is uploaded to the Web on a daily basis. However, search results are not always reliable due to the vast amount of data available on-line. As a result, users often have to repeat their searches in order to find exactly what they are looking for. To remedy this, some researchers have suggested recommendation systems. Since a recommendation system proposes information relevant to a particular query, users no longer need to repeat a search to obtain desired data. In the Web 2.0 era, recommendation systems often rely on the collaborative filtering approach, which is based on user information such as age, location, or preference. However, the traditional approach is affected by the cold-start and sparsity problems. The reason for these problems is the fact that the traditional system requires user information to operate properly. In this paper we address the sparsity problem associated with the current recommendation systems. We also suggest a new recommendation system approach and compare the performance of the proposed method with that of the traditional approach.