research-article

An efficient cold start solution based on group interests for recommender systems

Authors:

Bilal Hawashin,

Farshad FotouhiAuthors Info & Claims

DATA '18: Proceedings of the First International Conference on Data Science, E-learning and Information Systems

Article No.: 26, Pages 1 - 5

https://doi.org/10.1145/3279996.3280022

Published: 01 October 2018 Publication History

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Abstract

This paper proposes an efficient solution for the cold start problem in recommender systems. This problem occurs with new users who do not have sufficient information in their records. This would cause the recommender system to fail in providing recommendations to these users. This problem is one of the common and important problems in recommender systems. Although some solutions have been proposed to solve it in the literature, these solutions would not work properly in some scenarios because they do not concentrate on finding the actual interests of the users and the hidden motives behind their behavior. Our proposed solution uses the hidden interests of the group to which the target user belongs to provide recommendations for that user. The experiments show that our proposed solution is efficient in terms of searching time and space consumption.

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Cited By

Yuan ZPan JZhang XChen D(2024)HomeSGN: A Smarter Home with Novel Rule Mining Enabled by a Scorer-Generator GAN2024 29th Asia and South Pacific Design Automation Conference (ASP-DAC)10.1109/ASP-DAC58780.2024.10473909(102-108)Online publication date: 22-Jan-2024
https://doi.org/10.1109/ASP-DAC58780.2024.10473909
Shaikh SSuresh Kumar BNarang G(2022)Recommender system for health care analysis using machine learning technique: a reviewTheoretical Issues in Ergonomics Science10.1080/1463922X.2022.206107823:5(613-642)Online publication date: 22-Apr-2022
https://doi.org/10.1080/1463922X.2022.2061078
Hawashin BMansour AFotouhi FAlZu'bi SKanan T(2021)A Novel Recommender System Using Interest Extracting Agents and User Feedback2021 International Conference on Information Technology (ICIT)10.1109/ICIT52682.2021.9491654(674-678)Online publication date: 14-Jul-2021
https://doi.org/10.1109/ICIT52682.2021.9491654
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Index Terms

An efficient cold start solution based on group interests for recommender systems
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
  2. Machine learning
    1. Machine learning algorithms
      1. Feature selection

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cover image ACM Other conferences

DATA '18: Proceedings of the First International Conference on Data Science, E-learning and Information Systems

October 2018

274 pages

ISBN:9781450365369

DOI:10.1145/3279996

Conference Chair:
Juan Alfonso Lara Torralbo
UDIMA, Spain
,
Program Chair:
Shadi A. Aljawarneh
JUST, Jordan

Copyright © 2018 ACM.

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Published: 01 October 2018

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DATA '18

DATA '18: International Conference on Data Science, E-learning and Information Systems 2018

October 1 - 2, 2018

Madrid, Spain

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Cited By

Yuan ZPan JZhang XChen D(2024)HomeSGN: A Smarter Home with Novel Rule Mining Enabled by a Scorer-Generator GAN2024 29th Asia and South Pacific Design Automation Conference (ASP-DAC)10.1109/ASP-DAC58780.2024.10473909(102-108)Online publication date: 22-Jan-2024
https://doi.org/10.1109/ASP-DAC58780.2024.10473909
Shaikh SSuresh Kumar BNarang G(2022)Recommender system for health care analysis using machine learning technique: a reviewTheoretical Issues in Ergonomics Science10.1080/1463922X.2022.206107823:5(613-642)Online publication date: 22-Apr-2022
https://doi.org/10.1080/1463922X.2022.2061078
Hawashin BMansour AFotouhi FAlZu'bi SKanan T(2021)A Novel Recommender System Using Interest Extracting Agents and User Feedback2021 International Conference on Information Technology (ICIT)10.1109/ICIT52682.2021.9491654(674-678)Online publication date: 14-Jul-2021
https://doi.org/10.1109/ICIT52682.2021.9491654
El Kouni IKaroui WRomdhane L(2021)A Novel Structural and Semantic Similarity in Social Recommender SystemsComplex, Intelligent and Software Intensive Systems10.1007/978-3-030-79725-6_3(23-34)Online publication date: 30-Jun-2021
https://doi.org/10.1007/978-3-030-79725-6_3
Hawashin BAlzubi SMughaid AFotouhi FAbusukhon A(2020)An Efficient Cold Start Solution for Recommender Systems Based on Machine Learning and User Interests2020 Seventh International Conference on Software Defined Systems (SDS)10.1109/SDS49854.2020.9143953(220-225)Online publication date: Apr-2020
https://doi.org/10.1109/SDS49854.2020.9143953
Alzubi SHawashin BMughaid AJararweh Y(2020)Whats Trending? An Efficient Trending Research Topics Extractor and Recommender2020 11th International Conference on Information and Communication Systems (ICICS)10.1109/ICICS49469.2020.239519(191-196)Online publication date: Apr-2020
https://doi.org/10.1109/ICICS49469.2020.239519
Motevallian SHasheminejad SAhmadi H(2020)Reliability Estimating By Demographic Matrix in Item-based Recommender Systems2020 10th International Conference on Computer and Knowledge Engineering (ICCKE)10.1109/ICCKE50421.2020.9303704(076-081)Online publication date: 29-Oct-2020
https://doi.org/10.1109/ICCKE50421.2020.9303704
Hawashin BLafi MKanan TMansour A(2019)An efficient hybrid similarity measure based on user interests for recommender systemsExpert Systems10.1111/exsy.1247137:5Online publication date: 29-Aug-2019
https://doi.org/10.1111/exsy.12471
Kanan TSadaqa OAlmhirat AKanan E(2019)Arabic Light Stemming: A Comparative Study between P-Stemmer, Khoja Stemmer, and Light10 Stemmer2019 Sixth International Conference on Social Networks Analysis, Management and Security (SNAMS)10.1109/SNAMS.2019.8931842(511-515)Online publication date: Oct-2019
https://doi.org/10.1109/SNAMS.2019.8931842
Hawashin BAqel DAlZu'bi SJararweh Y(2019)Novel Weighted Interest Similarity Measurement for Recommender Systems Using Rating Timestamp2019 Sixth International Conference on Software Defined Systems (SDS)10.1109/SDS.2019.8768548(166-170)Online publication date: Jun-2019
https://doi.org/10.1109/SDS.2019.8768548
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