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AI and Machine Learning in Recommender Systems and Customer Behavior

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A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (15 November 2025) | Viewed by 8284

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Special Issue Editors

Dr. Qinglong Li

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Guest Editor

Division of Computer Engineering, Hansung University, Seoul 02876, Republic of Korea
Interests: recommender system; multimodal AI; LLM; deep learning
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Jae Kyeong Kim

E-Mail Website
Guest Editor

Department of Big Data Analytics and the School of Management, Kyung Hee University, Seoul 02447, Republic of Korea
Interests: recommender systems; big data analytics; machine learning; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The integration of artificial intelligence (AI) and machine learning (ML) into recommender systems has significantly transformed the provision of personalized services. Nevertheless, understanding customer behavior remains a crucial aspect that underlies the efficacy of these systems. This Special Issue seeks to investigate technological advancements in AI-/ML-driven recommendation technologies and the theoretical foundations of customer behavior analysis that inform and guide these innovations.

In recent years, the exponential growth of digital data has necessitated more sophisticated algorithms to process and analyze user interactions, preferences, and behaviors. While AI and ML play a critical role in optimizing these processes, understanding the cognitive and behavioral patterns of customers is equally important for developing effective customer-centered recommender systems. Thus, we invite empirical and theoretical research addressing these dual aspects: the development of advanced recommender system technologies and the underlying behavioral theories that enhance personalization strategies.

Topics of interest include, but are not limited to, the following:

AI and ML techniques for recommender systems;
Theoretical frameworks for understanding and predicting customer behavior;
Behavioral models in personalized service;
Multimodal data integration for enhanced recommendation accuracy;
Solutions for data sparsity and cold-start problems in recommender systems;
Transfer learning, domain adaptation, and personalization in recommendations;
Ethical issues, fairness, and bias in AI-driven personalization;
Case studies and applications of recommender systems across industries.

Dr. Qinglong Li
Prof. Dr. Jae Kyeong Kim
Guest Editors

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Keywords

machine learning applications in personalization
deep learning techniques in personalization
recommender systems in business contexts
predictive analytics in personalization
predicting user behavior in personalization

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Published Papers (3 papers)

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Research

19 pages, 358 KB

Open AccessArticle

Edge-Level Forest Fire Prediction with Selective Communication in Hierarchical Wireless Sensor Networks

by Ahshanul Haque and Hamdy Soliman

Electronics 2026, 15(4), 881; https://doi.org/10.3390/electronics15040881 - 20 Feb 2026

Viewed by 450

Abstract

Wildfire events are increasing in frequency and severity, creating an urgent need for early, accurate, and energy-efficient forest fire prediction systems that can operate at a large scale. A fundamental challenge in edge-level forest fire prediction lies in jointly achieving high detection accuracy while minimizing wireless transmissions and communication-related energy consumption. This paper proposes a communication-aware hierarchical wireless sensor network (WSN) framework that performs fire versus normal environmental state classification directly at the network edge. Multi-modal physical and constrained virtual sensor readings are fused into short-term temporal supervectors and processed locally using lightweight random forest classifiers deployed on sensor nodes and cluster heads. A temporal 2-of-3 voting mechanism is applied at the edge to suppress transient noise and improve prediction reliability before triggering communication. The proposed design enables selective, event-driven transmission, where only temporally validated abnormal states are forwarded through the hierarchy, thereby decoupling detection accuracy from continuous data reporting. Extensive experiments using real multi-modal environmental sensor data and statistically rigorous 5-fold GroupKFold cross-validation—ensuring strict node-level separation between training and testing—demonstrate the effectiveness of the approach. The proposed framework achieves a node-level accuracy of 98.82 ± 1.75% and a scenario-level detection accuracy of 96.52 ± 0.89%. Compared to periodic reporting and the LEACH protocol, the system reduces wireless transmissions by over 66% and communication-related energy consumption by more than 66% across network sizes ranging from 100 to 1000 nodes. The main contributions of this work are summarized as follows: (1) a communication-aware hierarchical Edge-AI framework for early forest fire prediction that performs local inference and temporal validation directly at sensor nodes; (2) a constrained virtual sensing strategy integrated with temporal supervector modeling to enhance spatial coverage while preserving reliability; and (3) a statistically rigorous large-scale evaluation demonstrating joint optimization of prediction accuracy, transmission reduction, and communication energy efficiency across network sizes ranging from 100 to 1000 nodes. These results show that accurate early forest fire prediction can be achieved through edge-level inference and selective communication, substantially extending network lifetime while maintaining statistically reliable detection performance. Full article

(This article belongs to the Special Issue AI and Machine Learning in Recommender Systems and Customer Behavior)

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22 pages, 1961 KB

Open AccessArticle

Incorporating Implicit and Explicit Feature Fusion into Hybrid Recommendation for Improved Rating Prediction

by Qinglong Li, Euiju Jeong, Seok-Kee Lee and Jiaen Li

Electronics 2025, 14(12), 2384; https://doi.org/10.3390/electronics14122384 - 11 Jun 2025

Viewed by 1409

Abstract

Online review texts serve as a valuable source of auxiliary information for addressing the data sparsity problem in recommender systems. These reviews often reflect user preferences across multiple item attributes and can be effectively incorporated into recommendation models to enhance both the accuracy and interpretability of recommendations. Review-based recommendation approaches can be broadly classified into implicit and explicit methods. Implicit methods leverage deep learning techniques to extract latent semantic representations from review texts but generally lack interpretability due to limited transparency in the training process. In contrast, explicit methods rely on hand-crafted features derived from domain knowledge, which offer high explanatory capability but typically capture only shallow information. Integrating the complementary strengths of these two approaches presents a promising direction for improving recommendation performance. However, previous research exploring this integration remains limited. In this study, we propose a novel recommendation model that jointly considers implicit and explicit representations derived from review texts. To this end, we incorporate a self-attention mechanism to emphasize important features from each representation type and utilize Bidirectional Encoder Representations from Transformers (BERT) to capture rich contextual information embedded in the reviews. We evaluate the performance of the proposed model through extensive experiments using three real-world datasets. The experimental results demonstrate that our model outperforms several baseline models, confirming its effectiveness in generating accurate and explainable recommendations. Full article

(This article belongs to the Special Issue AI and Machine Learning in Recommender Systems and Customer Behavior)

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21 pages, 4292 KB

Open AccessArticle

A Deep-Reinforcement-Learning-Based Multi-Source Information Fusion Portfolio Management Approach via Sector Rotation

by Yuxiao Yan, Changsheng Zhang, Yang An and Bin Zhang

Electronics 2025, 14(5), 1036; https://doi.org/10.3390/electronics14051036 - 5 Mar 2025

Cited by 5 | Viewed by 5585

Abstract

As a research objective in quantitative trading, the aim of portfolio management is to find the optimal allocation of funds by following the dynamic changes in stock prices. The principal issue with current portfolio management methods is their narrow focus on a single data source, neglecting the changes or news arising from sectors. Methods for integrating news data frequently face challenges with regard to quantifying text data and embedding them into portfolio models; this process often necessitates considerable manual labeling. To address these issues, we proposed a sector rotation portfolio management approach based on deep reinforcement learning (DRL) via multi-source information. The multi-source information includes the temporal data of sector and stock features, as well as news data. In terms of structure, in this method, a dual-layer reinforcement learning structure is deployed, comprising a multi-agent sector layer and a graph convolution layer. The former learns the trend of sectors, while the latter learns the connections between stocks in sectors, and the impact of news on sectors is integrated through large language models without manual labeling or fusing output information of other modules to provide the final portfolio management scheme. The results of simulation experiments on the Chinese and US (United States) stock markets show that our method demonstrates significant improvements over multiple state-of-the-art approaches. Full article

(This article belongs to the Special Issue AI and Machine Learning in Recommender Systems and Customer Behavior)

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