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research-article

Hybrid recommender systems: : A systematic literature review

Authors:

Erion Çano, Maurizio MorisioAuthors Info & Claims

Intelligent Data Analysis, Volume 21, Issue 6

Pages 1487 - 1524

https://doi.org/10.3233/IDA-163209

Published: 01 January 2017 Publication History

Abstract

Recommender systems are software tools used to generate and provide suggestions for items and other entities to the users by exploiting various strategies. Hybrid recommender systems combine two or more recommendation strategies in different ways to benefit from their complementary advantages. This systematic literature review presents the state of the art in hybrid recommender systems of the last decade. It is the first quantitative review work completely focused in hybrid recommenders. We address the most relevant problems considered and present the associated data mining and recommendation techniques used to overcome them. We also explore the hybridization classes each hybrid recommender belongs to, the application domains, the evaluation process and proposed future research directions. Based on our findings, most of the studies combine collaborative filtering with another technique often in a weighted way. Also cold-start and data sparsity are the two traditional and top problems being addressed in 23 and 22 studies each, while movies and movie datasets are still widely used by most of the authors. As most of the studies are evaluated by comparisons with similar methods using accuracy metrics, providing more credible and user oriented evaluations remains a typical challenge. Besides this, newer challenges were also identified such as responding to the variation of user context, evolving user tastes or providing cross-domain recommendations. Being a hot topic, hybrid recommenders represent a good basis with which to respond accordingly by exploring newer opportunities such as contextualizing recommendations, involving parallel hybrid algorithms, processing larger datasets, etc.

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Cited By

Nakhipova VKerimbekov YUmarova ZBulbul HSuleimenova LAdylbekova E(2024)Integration of Collaborative Filtering Into Naive Bayes Method to Enhance Student Performance PredictionInternational Journal of Information and Communication Technology Education10.4018/IJICTE.35251220:1(1-18)Online publication date: 17-Sep-2024
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Kulvinder Singh Dhawan SBali N(2024)An Ensemble Learning Hybrid Recommendation System Using Content-Based, Collaborative Filtering, Supervised Learning and Boosting AlgorithmsAutomatic Control and Computer Sciences10.3103/S014641162470061558:5(491-505)Online publication date: 1-Oct-2024
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Index Terms

Hybrid recommender systems: A systematic literature review
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
  2. Information systems applications

Index terms have been assigned to the content through auto-classification.

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Published In

cover image Intelligent Data Analysis

Intelligent Data Analysis Volume 21, Issue 6

2017

219 pages

ISSN:1088-467X

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© 2017 – IOS Press and the authors. All rights reserved.

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IOS Press

Netherlands

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Published: 01 January 2017

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Nakhipova VKerimbekov YUmarova ZBulbul HSuleimenova LAdylbekova E(2024)Integration of Collaborative Filtering Into Naive Bayes Method to Enhance Student Performance PredictionInternational Journal of Information and Communication Technology Education10.4018/IJICTE.35251220:1(1-18)Online publication date: 17-Sep-2024
https://dl.acm.org/doi/10.4018/IJICTE.352512
Kulvinder Singh Dhawan SBali N(2024)An Ensemble Learning Hybrid Recommendation System Using Content-Based, Collaborative Filtering, Supervised Learning and Boosting AlgorithmsAutomatic Control and Computer Sciences10.3103/S014641162470061558:5(491-505)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.3103/S0146411624700615
Alsayed ADam HNguyen CSpinellis DConstantinou EBacchelli A(2024)MicroRec: Leveraging Large Language Models for Microservice RecommendationProceedings of the 21st International Conference on Mining Software Repositories10.1145/3643991.3644916(419-430)Online publication date: 15-Apr-2024
https://dl.acm.org/doi/10.1145/3643991.3644916
Sharma SKumar Shakya H(2024)Hybrid recommendation system for movies using artificial neural networkExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.125194258:COnline publication date: 15-Dec-2024
https://dl.acm.org/doi/10.1016/j.eswa.2024.125194
Zhang JZain AZhou KChen XZhang R(2024)A review of recommender systems based on knowledge graph embeddingExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.123876250:COnline publication date: 18-Jul-2024
https://dl.acm.org/doi/10.1016/j.eswa.2024.123876
Marana Fda Silva Fernandes RGuzmán Jde Leon Ferreira de Carvalho ACúri M(2024)What rating they will probably giveExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.122981245:COnline publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.122981
Fink LNewman LHaran U(2024)Let me decideComputers in Human Behavior10.1016/j.chb.2024.108244156:COnline publication date: 9-Jul-2024
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Valmorisco SRaya LSanchez A(2024)Enabling personalized VR experiences: a framework for real-time adaptation and recommendations in VR environmentsVirtual Reality10.1007/s10055-024-01020-028:3Online publication date: 26-Jun-2024
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Sánchez-Corcuera RCasado-Mansilla DBorges CLópez-de-Ipiña D(2024)Persuasion-based recommender system ensambling matrix factorisation and active learning modelsPersonal and Ubiquitous Computing10.1007/s00779-020-01382-728:1(247-257)Online publication date: 1-Feb-2024
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Miehling ENair RDaly ERamamurthy KRedmond ROh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Cookie consent has disparate impact on estimation accuracyProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3667610(34308-34328)Online publication date: 10-Dec-2023
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