research-article

Hybrid Recommendation System using Particle Swarm Optimization and User Access Based Ranking

Authors:

S. Sendhilkumar,

G. S. MahalakshmiAuthors Info & Claims

ICIA-16: Proceedings of the International Conference on Informatics and Analytics

Article No.: 68, Pages 1 - 9

https://doi.org/10.1145/2980258.2980405

Published: 25 August 2016 Publication History

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Abstract

This paper introduces a novel architecture for a new user recommendation system which is based on Particle Swarm Optimization (PSO) algorithm and User Access Based Ranking (UABR) approach. The Recommendation System (RS) is an efficient tool for providing the relevant pages to the users. A vital issue for the RS that has enormously captured the attention of researchers is the cold-start problem. This issue is related to recommendations for new users. For new users, the system does not have data about their preferences in order to make recommendations. We proposed a technique with the swarm intelligence approach of Particle Swarm Optimization in combination with User access based ranking algorithm for the new user recommendation. The PSO algorithm is applied to user grouping. Using this approach, users with similar searching travels are gathered into the same cluster. Recommendations for new user are produced through the user access based ranking algorithm. The results of experiments exhibits that the proposed strategy can effectively enhance the quality of recommendation with the better precision, recall and F_Score values.

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Cited By

Duong TCao TDo TMai TTran M(2023)A Scalable Recommendation System with Hybrid Similarity Matrix Using Accelerated Particle Swarm Optimization2023 International Conference on Advanced Technologies for Communications (ATC)10.1109/ATC58710.2023.10318874(480-487)Online publication date: 19-Oct-2023
https://doi.org/10.1109/ATC58710.2023.10318874
Gasmi IAzizi MSeridi-Bouchelaghem HAzizi NBelhaouari S(2021)Enhanced context-aware recommendation using topic modeling and particle swarm optimizationJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-21033140:6(12227-12242)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.3233/JIFS-210331
Wasid MAli R(2021)A frequency count approach to multi-criteria recommender system based on criteria weighting using particle swarm optimizationApplied Soft Computing10.1016/j.asoc.2021.107782112(107782)Online publication date: Nov-2021
https://doi.org/10.1016/j.asoc.2021.107782
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cover image ACM Other conferences

ICIA-16: Proceedings of the International Conference on Informatics and Analytics

August 2016

868 pages

ISBN:9781450347563

DOI:10.1145/2980258

Copyright © 2016 ACM.

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Published: 25 August 2016

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ICIA-16

ICIA-16: International Conference on Informatics and Analytics

August 25 - 26, 2016

Pondicherry, India

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Cited By

Duong TCao TDo TMai TTran M(2023)A Scalable Recommendation System with Hybrid Similarity Matrix Using Accelerated Particle Swarm Optimization2023 International Conference on Advanced Technologies for Communications (ATC)10.1109/ATC58710.2023.10318874(480-487)Online publication date: 19-Oct-2023
https://doi.org/10.1109/ATC58710.2023.10318874
Gasmi IAzizi MSeridi-Bouchelaghem HAzizi NBelhaouari S(2021)Enhanced context-aware recommendation using topic modeling and particle swarm optimizationJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-21033140:6(12227-12242)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.3233/JIFS-210331
Wasid MAli R(2021)A frequency count approach to multi-criteria recommender system based on criteria weighting using particle swarm optimizationApplied Soft Computing10.1016/j.asoc.2021.107782112(107782)Online publication date: Nov-2021
https://doi.org/10.1016/j.asoc.2021.107782
Ayub MGhazanfar MMehmood ZAlyoubi KAlfakeeh A(2020)Unifying user similarity and social trust to generate powerful recommendations for smart cities using collaborating filtering-based recommender systemsSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-019-04588-x24:15(11071-11094)Online publication date: 1-Aug-2020
https://dl.acm.org/doi/10.1007/s00500-019-04588-x
Logesh RSubramaniyaswamy VVijayakumar VGao XWang G(2019)Hybrid bio-inspired user clustering for the generation of diversified recommendationsNeural Computing and Applications10.1007/s00521-019-04128-632:7(2487-2506)Online publication date: 15-Mar-2019
https://doi.org/10.1007/s00521-019-04128-6

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