Journal article
A reliability-enhanced deep ensemble learning framework for recommendation
Information & management, Vol.63(2), 104296
03/2026
DOI: 10.1016/j.im.2025.104296
Abstract
With the rapid development of Internet technology, information overload has become increasingly severe. Recommendation systems, as effective information filtering tools, can provide users with personalized recommendations to reduce their decision-making costs. Although recommendation algorithms have achieved great progress in prediction accuracy with the advance of deep learning technologies, the reliability of recommendation results has been relatively underexplored. "Reliability" refers to the likelihood of users accepting the final recommended results, and it is one of the important aspects influencing user experience. Many existing recommendation systems primarily optimize predictive accuracy metrics, while relatively fewer works explicitly model the likelihood of user acceptance, leading to potential gaps in user satisfaction. To address this, we propose the deep ensemble reliable recommendation algorithm (DERA). DERA integrates reliability into both the data preprocessing and model training phases of recommendation models. Drawing on the ensemble learning concept, DERA trains multiple weak learners and employs voting to determine the final prediction result. Additionally, a data preprocessing method is designed to alleviate the imbalance of training data. Experiments on four real-world datasets demonstrate that DERA can enhance recommendation performance by considering reliability. In summary, our work presents a novel framework to integrate reliability estimation into the training pipeline without extra side-information.
Details
- Title: Subtitle
- A reliability-enhanced deep ensemble learning framework for recommendation
- Creators
- Wenhua Li - Tianjin UniversityHongtao Li - Tianjin UniversityJunpeng Guo - Tianjin UniversityWeiguo Fan - Tippie College of Business University of Iowa, 108 John Pappajohn Business Building, S262 Iowa City, IA, 52242-1994, USA
- Resource Type
- Journal article
- Publication Details
- Information & management, Vol.63(2), 104296
- DOI
- 10.1016/j.im.2025.104296
- ISSN
- 0378-7206
- eISSN
- 1872-7530
- Publisher
- Elsevier B.V
- Grant note
- National Natural Science Foundation of China: 72171165 Chinese Ministry of Education of Humanities and Social Science Fund: 21YJA630021
The authors sincerely thank the Editorial Team for their guidance and support at every stage of the reviewing process. We are also grateful to the anonymous reviewers, whose perceptive and constructive feed-back has greatly strengthened this manuscript. This work is supported by the National Natural Science Foundation of China (Grant 72171165) and the Chinese Ministry of Education of Humanities and Social Science Fund (Grant 21YJA630021) .
- Language
- English
- Date published
- 03/2026
- Academic Unit
- Business Analytics
- Record Identifier
- 9985112975702771
Metrics
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