| Autor: |
Balan, Shilpa, Howell, Pamella, Choksi, Yash |
| Předmět: |
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| Zdroj: |
ACET Journal of Computer Education & Research; 2020, Vol. 14 Issue 1, p1-10, 10p |
| Abstrakt: |
Users have online access to millions of audio tracks and movies. Online streaming platforms widely use AI-based recommender systems to help users choose the songs to listen to and the movies to watch. As the volume of available content rises, a standardized methodology to evaluate recommender systems is required. This paper focuses on collaborative-based filtering method of recommender systems. Leveraging the matrix factorization technique, we provide comprehensive information on an algorithm for improving prediction accuracy using standardized Python code and validated with the MovieLens data set. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Supplemental Index |
| Externí odkaz: |
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