GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
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
What is actually taking place on web sites: e-commerce lessons from web server logs
Proceedings of the 2nd ACM conference on Electronic commerce
Proceedings of the 6th international conference on Intelligent user interfaces
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Selecting relevant instances for efficient and accurate collaborative filtering
Proceedings of the tenth international conference on Information and knowledge management
Eigentaste: A Constant Time Collaborative Filtering Algorithm
Information Retrieval
Learning Collaborative Information Filters
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Similarity measure and instance selection for collaborative filtering
WWW '03 Proceedings of the 12th international conference on World Wide Web
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)
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
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
A Bayesian approach to learning Bayesian networks with local structure
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
ItemRank: a random-walk based scoring algorithm for recommender engines
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
How does high dimensionality affect collaborative filtering?
Proceedings of the third ACM conference on Recommender systems
Performance evaluation of classification methods in cultural modeling
ISI'09 Proceedings of the 2009 IEEE international conference on Intelligence and security informatics
A random-walk based scoring algorithm applied to recommender engines
WebKDD'06 Proceedings of the 8th Knowledge discovery on the web international conference on Advances in web mining and web usage analysis
Collaborative filtering based on an iterative prediction method to alleviate the sparsity problem
Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services
Guest editorial: special issue on a decade of mining the Web
Data Mining and Knowledge Discovery
Data sparsity: a key disadvantage of user-based collaborative filtering?
APWeb'12 Proceedings of the 14th Asia-Pacific international conference on Web Technologies and Applications
A new user similarity model to improve the accuracy of collaborative filtering
Knowledge-Based Systems
Personalized recommendation based on review topics
Service Oriented Computing and Applications
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With the amount of available information on the Web growing rapidly with each day, the need to automatically filter the information in order to ensure greater user efficiency has emerged. Within the fields of user profiling and Web personalization several popular content filtering techniques have been developed. In this chapter we present one of such techniques – collaborative filtering. Apart from giving an overview of collaborative filtering approaches, we present the experimental results of confronting the k-Nearest Neighbor (kNN) algorithm with Support Vector Machine (SVM) in the collaborative filtering framework using datasets with different properties. While the k-Nearest Neighbor algorithm is usually used for collaborative filtering tasks, Support Vector Machine is considered a state-of-the-art classification algorithm. Since collaborative filtering can also be interpreted as a classification/regression task, virtually any supervised learning algorithm (such as SVM) can also be applied. Experiments were performed on two standard, publicly available datasets and, on the other hand, on a real-life corporate dataset that does not fit the profile of ideal data for collaborative filtering. We conclude that the quality of collaborative filtering recommendations is highly dependent on the sparsity of available data. Furthermore, we show that kNN is dominant on datasets with relatively low sparsity while SVM-based approaches may perform better on highly sparse data.