A note on genetic algorithms for large-scale feature selection
Pattern Recognition Letters
Fab: content-based, collaborative recommendation
Communications of the ACM
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Prototype selection for the nearest neighbour rule through proximity graphs
Pattern Recognition Letters
CSCW '98 Proceedings of the 1998 ACM conference on Computer supported cooperative work
Selection of the optimal prototype subset for 1-NN classification
Pattern Recognition Letters
Data mining: concepts and techniques
Data mining: concepts and techniques
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Personalization of Supermarket Product Recommendations
Data Mining and Knowledge Discovery
E-Commerce Recommendation Applications
Data Mining and Knowledge Discovery
Eigentaste: A Constant Time Collaborative Filtering Algorithm
Information Retrieval
Improving Minority Class Prediction Using Case-Specific Feature Weights
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Learning Collaborative Information Filters
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
ICTAI '00 Proceedings of the 12th IEEE International Conference on Tools with Artificial Intelligence
Knowledge and Information Systems
Toward Global Optimization of Case-Based Reasoning Systems for Financial Forecasting
Applied Intelligence
VISCORS: A Visual-Content Recommender for the Mobile Web
IEEE Intelligent Systems
Similarity Measure and Instance Selection for Collaborative Filtering
International Journal of Electronic Commerce
Utilizing Popularity Characteristics for Product Recommendation
International Journal of Electronic Commerce
Applications of wavelet data reduction in a recommender system
Expert Systems with Applications: An International Journal
An iterative semi-explicit rating method for building collaborative recommender systems
Expert Systems with Applications: An International Journal
A collaborative filtering method based on artificial immune network
Expert Systems with Applications: An International Journal
Collaborative filtering using orthogonal nonnegative matrix tri-factorization
Information Processing and Management: an International Journal
Collaborative filtering based on iterative principal component analysis
Expert Systems with Applications: An International Journal
Predicting Missing Ratings in Recommender Systems: Adapted Factorization Approach
International Journal of Electronic Commerce
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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Collaborative filtering (CF) is regarded as one of the most popular recommendation methods. However, CF has some significant weaknesses, such as problems of sparsity and scalability. Sparsity causes inaccuracy in the formation of neighbors with similar interests, and scalability prevents CF from scaling up with increases in the number of users and/or items. To mitigate these problems, this study proposes a hybrid CF and genetic algorithm (GA) model. GAs are widely believed to be effective on NP-complete global optimization problems, and they can provide good suboptimal solutions in a reasonable amount of time. In this study, the GA searches for relevant users and items from a user-item matrix not only to condense the matrix but also to improve the prediction accuracy. The reduced user-item matrix may reduce the sparsity problem by increasing the likelihood that different customers rate common items. It also shrinks the search space for CF, which ameliorates the scalability problem. Experimental results show that the proposed model improves performance and speed compared to the typical CF model.