The Effect of Neighborhood Selection on Collaborative Filtering and a Novel Hybrid Algorithm
| Autor: | Musa Milli, Hasan Bulut |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Computer science
business.industry Research areas 02 engineering and technology Recommender system Machine learning computer.software_genre Hybrid algorithm Theoretical Computer Science k-nearest neighbors algorithm Computational Theory and Mathematics Artificial Intelligence 020204 information systems 0202 electrical engineering electronic engineering information engineering Collaborative filtering 020201 artificial intelligence & image processing Artificial intelligence Data mining business computer Software Selection (genetic algorithm) |
| Zdroj: | Intelligent Automation & Soft Computing. 23:261-269 |
| ISSN: | 2326-005X 1079-8587 |
| DOI: | 10.1080/10798587.2016.1204776 |
| Popis: | Recommender systems are widely used in industry and are still active research areas in academia. For many businesses, they have become indispensable business tools. Producing accurate results for such systems is important for the operations of the businesses. For this reason, various algorithms and approaches have been developed for recommender systems to increase the prediction accuracy. Collaborative filtering is one of the most successful approaches. In collaborative filtering, in order to predict more accurately, it is recommended to determine user’s active neighbors. k-nearest neighbor (k-NN) algorithm is one of the most widely used neighbor selection algorithms. However, k-NN algorithm uses a fixed k value that reduces the accuracy of the prediction. In this paper, we present two novel approaches to increase the prediction accuracy of recommender systems; k%-nearest neighbor (k%-NN) algorithm to determine the appropriate k value for a user and a hybrid algorithm that combines a collaborative... |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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