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A Particle Swarm Optimization Approach to Multi Criteria Recommender System Utilizing Effective Similarity Measures

Published: 24 February 2017 Publication History

Abstract

Recommender system (RS), a web personalization tool, attempts to generate suitable recommendations to users based on their preferences. Generally, recommender system works on overall ratings but these ratings do not reflect the actual user preferences. Therefore, incorporation of multiple criteria ratings into RS can capture the user preferences accurately and produce effective recommendations to users. Multi criteria recommender systems (MCRS) generate recommendations to users based on the aggregation of similarities computed on multiple criteria using collaborative filtering. However, capturing optimal weights of various users on different criteria in the process of similarity aggregation is a major concern. Further selection of appropriate similarity measure is another challenge for employing collaborative filtering. Our work in this paper is an attempt towards developing multi criteria recommender systems by utilizing various similarity measures and particle swarm optimization to learn optimal weights. Experimental results reveal that our proposed approaches outperform other traditional approaches.

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  • (2025)Soft computing techniques in multi-criteria recommender systems: A comprehensive reviewApplied Soft Computing10.1016/j.asoc.2024.112579169(112579)Online publication date: Jan-2025
  • (2023)Time Cluster Personalized Ranking Recommender System in Multi-CloudMathematics10.3390/math1106130011:6(1300)Online publication date: 8-Mar-2023
  • (2023)Adaptable inheritance-based prediction model for multi-criteria recommender systemMultimedia Tools and Applications10.1007/s11042-023-14728-z82:21(32421-32442)Online publication date: 10-Mar-2023
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cover image ACM Other conferences
ICMLC '17: Proceedings of the 9th International Conference on Machine Learning and Computing
February 2017
545 pages
ISBN:9781450348171
DOI:10.1145/3055635
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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  • Southwest Jiaotong University

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New York, NY, United States

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Published: 24 February 2017

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Author Tags

  1. Multi criteria recommender system
  2. collaborative filtering
  3. particle swarm optimization
  4. similarity measures

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Cited By

View all
  • (2025)Soft computing techniques in multi-criteria recommender systems: A comprehensive reviewApplied Soft Computing10.1016/j.asoc.2024.112579169(112579)Online publication date: Jan-2025
  • (2023)Time Cluster Personalized Ranking Recommender System in Multi-CloudMathematics10.3390/math1106130011:6(1300)Online publication date: 8-Mar-2023
  • (2023)Adaptable inheritance-based prediction model for multi-criteria recommender systemMultimedia Tools and Applications10.1007/s11042-023-14728-z82:21(32421-32442)Online publication date: 10-Mar-2023
  • (2022)A novel top-n recommendation method for multi-criteria collaborative filteringExpert Systems with Applications10.1016/j.eswa.2022.116695198(116695)Online publication date: Jul-2022
  • (2022)A model‐based approach to user preference discovery in multi‐criteria recommender system using genetic programmingConcurrency and Computation: Practice and Experience10.1002/cpe.689934:11Online publication date: 24-Feb-2022
  • (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
  • (2021)A Pareto Dominance Approach to Multi-criteria Recommender System Using PSO AlgorithmInternational Conference on Innovative Computing and Communications10.1007/978-981-16-2594-7_60(737-755)Online publication date: 18-Aug-2021
  • (2021)A Deep Autoencoder Based Multi-Criteria Recommender SystemProceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2021)10.1007/978-3-030-76346-6_6(56-65)Online publication date: 29-May-2021
  • (2020)A Review and Classification of Multi-Criteria Recommender Systems2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS)10.1109/ICICCS48265.2020.9120983(1156-1162)Online publication date: May-2020
  • (2020)A Comparative Analysis of Genetic Programming and Genetic Algorithm on Multi-Criteria Recommender Systems2020 5th International Conference on Communication and Electronics Systems (ICCES)10.1109/ICCES48766.2020.9138051(1338-1343)Online publication date: Jun-2020
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