A Comparative Analysis of Collaborative Filtering Similarity Measurements for Recommendation Systems

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Published: Mar 11, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i3s.6180
Keywords:
Recommendation System, Collaborative Filtering, Similarity Measurements, Model-Based, Memory-Based, Filtering Systems

Main Article Content

Praveen Kumar
Research Scholar, Department of CSE, Suresh Gyan Vihar University, Jaipur
Mukesh Kumar Gupta
Associate Dean Research, Suresh Gyan Vihar University, Jaipur
Channapragada Rama Seshagiri Rao
Department of CSE, Professor & Principal, Vignana Bharathi Engineering College, Chintapalliguda(v).Ibrahimpatnam
M Bhavsingh
Assistant Professor, Department of CSE, Ashoka Women’s Engineering College,Kurnool, Andhra Pradesh
M. Srilakshmi
Associate Professor, Department of CSE, G. Pullaiah College of Engineering and Technology (Autonomous), Kurnool

Abstract

Collaborative Filtering (CF) is a widely used technique in recommendation systems to suggest items to users based on their previous interactions with the system. CF involves finding correlations between the preferences of different users and using those correlations to provide recommendations. This technique can be divided into user-based and item-based CF, both of which utilize similarity metrics to generate recommendations. Content-based filtering is another commonly used recommendation technique that analyzes the attributes of items to suggest similar items. To enhance the accuracy of recommendation systems, hybrid algorithms that combine CF and content-based filtering techniques have been developed. These hybrid systems leverage the strengths of both approaches to provide more accurate and personalized recommendations. In conclusion, collaborative filtering is an essential technique in recommendation systems, and the use of various similarity metrics and hybrid techniques can enhance the quality of recommendations.

Article Details

How to Cite
Kumar, P. ., Gupta, M. K. ., Rao, C. R. S. ., Bhavsingh, M. ., & Srilakshmi, M. (2023). A Comparative Analysis of Collaborative Filtering Similarity Measurements for Recommendation Systems. International Journal on Recent and Innovation Trends in Computing and Communication, 11(3s), 184–192. https://doi.org/10.17762/ijritcc.v11i3s.6180
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Vol. 11 No. 3s (2023)
Section
Articles

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