Enhancing Recommender Systems through Hybrid Fusion of SVD/SVD++ and k-Nearest Neighbors-Reference-Cited by-同舟云学术

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Enhancing Recommender Systems through Hybrid Fusion of SVD/SVD++ and k-Nearest Neighbors

Published:2023-11-22 Issue: Volume:12 Page:1-7
ISSN:
Container-title:2023 14th International Conference on Intelligent Systems: Theories and Applications (SITA)
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Author:

Azri Nabil¹,Haddi Adil²,Azri Abdelghani¹

Affiliation:

1. Hassan First University of Settat, FST,LAVETE Laboratory,Settat,Morocco

2. Hassan First University of Settat, ENSA,LAVETE Laboratory,Berrechid,Morocco

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10373588/10373590/10373705.pdf?arnumber=10373705

Reference16 articles.

1. Recommender Systems Handbook

2. Hybrid recommender systems: Survey and experiments;Burke;User Modeling and User-Adapted Interaction,2002

3. Factorization meets the neighborhood

4. Collaborative Filtering Recommender Systems

5. Collaborative Filtering Recommender Systems

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Improving Recommendation System Accuracy Augmenting User Profile with Matrix Factorization;2024 International Conference on Communication, Computer Sciences and Engineering (IC3SE);2024-05-09

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