A Cognitive Similarity-Based Measure to Enhance the Performance of Collaborative Filtering-Based Recommendation System-Reference-Cited by-同舟云学术

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A Cognitive Similarity-Based Measure to Enhance the Performance of Collaborative Filtering-Based Recommendation System

Published:2022-12 Issue:6 Volume:9 Page:1785-1793
ISSN:2329-924X
Container-title:IEEE Transactions on Computational Social Systems
language:
Short-container-title:IEEE Trans. Comput. Soc. Syst.

Author:

Jain Gourav¹^ORCID,Mahara Tripti²^ORCID,Sharma S. C.³^ORCID,Sangaiah Arun Kumar⁴^ORCID

Affiliation:

1. Department of Polymer and Process Engineering, Electronic and Computer Discipline, IIT Roorkee, Roorkee, Uttarakhand, India

2. Department of Polymer and Process Engineering, IIT Roorkee, Roorkee, Uttarakhand, India

3. Department of Paper Technology, Electronic and Computer Discipline, IIT Roorkee, Roorkee, Uttarakhand, India

4. Department of Industrial Engineering and Management, National Yunlin University of Science and Technology, Douliu, Taiwan

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Human-Computer Interaction,Social Sciences (miscellaneous),Modeling and Simulation

Link

http://xplorestaging.ieee.org/ielx7/6570650/9966856/09829416.pdf?arnumber=9829416

Reference30 articles.

1. An Efficient Similarity Measure for User-Based Collaborative Filtering Recommender Systems Inspired by the Physical Resonance Principle

2. A new user similarity measure in a new prediction model for collaborative filtering

3. An efficient recommendation generation using relevant Jaccard similarity

4. An Effective Model for Jaccard Coefficient to Increase the Performance of Collaborative Filtering

5. Towards Cognitive Recommender Systems

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