GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Matrix computations (3rd ed.)
Eigentaste: A Constant Time Collaborative Filtering Algorithm
Information Retrieval
Expert Systems with Applications: An International Journal
Modeling relationships at multiple scales to improve accuracy of large recommender systems
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Eigentaste 5.0: constant-time adaptability in a recommender system using item clustering
Proceedings of the 2007 ACM conference on Recommender systems
A new restoration-based recommender system for shopping buddy smart carts
International Journal of Business Information Systems
A collaborative recommender system based on probabilistic inference from fuzzy observations
Fuzzy Sets and Systems
Using back-propagation to learn association rules for service personalization
Expert Systems with Applications: An International Journal
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Collaborative Recommendations Using Bayesian Networks and Linguistic Modelling
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
A Novel Recommending Algorithm Based on Topical PageRank
AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Factor in the neighbors: Scalable and accurate collaborative filtering
ACM Transactions on Knowledge Discovery from Data (TKDD)
Privacy-preserving eigentaste-based collaborative filtering
IWSEC'07 Proceedings of the Security 2nd international conference on Advances in information and computer security
A hybrid recommendation method with reduced data for large-scale application
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Collaborative filtering through SVD-based and hierarchical nonlinear PCA
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part I
Recommender system based on click stream data using association rule mining
Expert Systems with Applications: An International Journal
An improved privacy-preserving DWT-based collaborative filtering scheme
Expert Systems with Applications: An International Journal
Collaboratively shared information retrieval model for e-learning
ICWL'06 Proceedings of the 5th international conference on Advances in Web Based Learning
A strategy-oriented operation module for recommender systems in E-commerce
Computers and Operations Research
Collaborative Filtering with a User-Item Matrix Reduction Technique
International Journal of Electronic Commerce
A latent model for collaborative filtering
International Journal of Approximate Reasoning
A literature review and classification of recommender systems research
Expert Systems with Applications: An International Journal
An implementation and evaluation of recommender systems for traveling abroad
Expert Systems with Applications: An International Journal
Learning Rating Patterns for Top-N Recommendations
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Addressing cold-start in app recommendation: latent user models constructed from twitter followers
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Methods of expert estimations concordance for integral quality estimation
Expert Systems with Applications: An International Journal
| Hi-index | 12.06 |
Collaborative filtering (CF) is one of the most popular recommender system technologies, and utilizes the known preferences of a group of users to predict the unknown preference of a new user. However, the existing CF techniques has the drawback that it requires the entire existing data be maintained and analyzed repeatedly whenever new user ratings are added. To avoid such a problem, Eigentaste, a CF approach based on the principal component analysis (PCA), has been proposed. However, Eigentaste requires that each user rate every item in the so called gauge set for executing PCA, which may not be always feasible in practice. Developed in this article is an iterative PCA approach in which no gauge set is required, and singular value decomposition is employed for estimating missing ratings and dimensionality reduction. Principal component values for users in reduced dimension are used for clustering users. Then, the proposed approach is compared to Eigentaste in terms of the mean absolute error of prediction using the Jester, MovieLens, and EachMovie data sets. Experimental results show that the proposed approach, even without a gauge set, performs slightly better than Eigentaste regardless of the data set and clustering method employed, implying that it can be used as a useful alternative when defining a gauge set is neither possible nor practical.