A Game-Based Evolutionary Clustering With Historical Information Aggregation for Personal Recommendation-Reference-Cited by-同舟云学术

A Game-Based Evolutionary Clustering With Historical Information Aggregation for Personal Recommendation

Published:2023-04 Issue:2 Volume:7 Page:552-564
ISSN:2471-285X
Container-title:IEEE Transactions on Emerging Topics in Computational Intelligence
language:
Short-container-title:IEEE Trans. Emerg. Top. Comput. Intell.

Author:

Chen Jianrui¹^ORCID,Zhu Tingting²,Gong Maoguo³^ORCID,Wang Zhihui²

Affiliation:

1. Key Laboratory of Intelligent Computing and Service Technology for Folk Song, Ministry of Culture and Tourism, Xi’an, China

2. School of Computer Science, Shaanxi Normal University, Xi’an, China

3. School of Electronic Engineering, Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi’an, China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Shaanxi Province

Fundamental Research Funds for the Central Universities

Center for Applied Mathematics of Inner Mongolian

Shaanxi Key Science and Technology Innovation Team Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Computational Mathematics,Control and Optimization,Computer Science Applications

Link

http://xplorestaging.ieee.org/ielx7/7433297/10081151/09833392.pdf?arnumber=9833392

Reference44 articles.

1. A Knowledge-Based Recommendation System That Includes Sentiment Analysis and Deep Learning

2. Density peaks clustering based on k-nearest neighbors and self-recommendation

3. A Hybrid E-Learning Recommendation Approach Based on Learners’ Influence Propagation

4. A Partition-Based Partial Personalized Model for Points-of-Interest Recommendations

5. Locally differentially private item-based collaborative filtering

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