GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Recommendation as classification: using social and content-based information in recommendation
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
ACM Computing Surveys (CSUR)
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
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Eigentaste: A Constant Time Collaborative Filtering Algorithm
Information Retrieval
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Collaborative Filtering by Personality Diagnosis: A Hybrid Memory and Model-Based Approach
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
Scalable collaborative filtering using cluster-based smoothing
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Probabilistic latent semantic analysis
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
WebKDD'04 Proceedings of the 6th international conference on Knowledge Discovery on the Web: advances in Web Mining and Web Usage Analysis
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Collaborative Filtering (CF)-based recommender systems bring mutual benefits to both users and the operators of the sites with too much information. Users benefit as they are able to find items of interest from an unmanageable number of available items. On the other hand, e-commerce sites that employ recommender systems can increase sales revenue in at least two ways: a) by drawing customers' attention to items that they are likely to buy, and b) by cross-selling items. However, the sheer number of customers and items typical in e-commerce systems demand specially designed CF algorithms that can gracefully cope with the vast size of the data. Many algorithms proposed thus far, where the principal concern is recommendation quality, may be too expensive to operate in a large-scale system. We propose CLUSTKNN, a simple and intuitive algorithm that is well suited for large data sets. The method first compresses data tremendously by building a straightforward but efficient clustering model. Recommendations are then generated quickly by using a simple NEAREST NEIGHBOR-based approach. We demonstrate the feasibility of CLUSTKNN both analytically and empirically. We also show, by comparing with a number of other popular CF algorithms that, apart from being highly scalable and intuitive, CLUSTKNN provides very good recommendation accuracy as well.