Implementation and Effectiveness Evaluation of Four Common Algorithms of Recommendation Systems - User Collaboration Filter, Item-based Collaborative Filtering, Matrix Factorization and Neural Collaborative Filtering-Reference-Cited by-同舟云学术

Implementation and Effectiveness Evaluation of Four Common Algorithms of Recommendation Systems - User Collaboration Filter, Item-based Collaborative Filtering, Matrix Factorization and Neural Collaborative Filtering

Published:2022-09 Issue: Volume: Page:
ISSN:
Container-title:2022 International Conference on Cloud Computing, Big Data Applications and Software Engineering (CBASE)
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Author:

Liu Hongjiao¹

Affiliation:

1. University of Tasmania,Department of Information and Communication Technology (ICT),Hobart,Australia,7005

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10092532/10092535/10092591.pdf?arnumber=10092591

Reference13 articles.

1. Improving the Estimation of Tail Ratings in Recommender System with Multi-Latent Representations

2. Interest before liking: Two-step recommendation approaches

3. Incorporating temporal dynamics into LDA for one-class collaborative filtering

4. A survey of recommendation system: Research challenges;sharma;International Journal of Engineering Trends and Technology (IJETT),2013

5. Personalized Recommendation System Based on Collaborative Filtering for IoT Scenarios

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