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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 October 2023) | Viewed by 13987

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Special Issue Editors

Prof. Dr. Jesús G. Boticario

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Guest Editor

aDeNu Research Group, Artificial Intelligence Department, Computer Science School, UNED, 28040 Madrid, Spain
Interests: artificial intelligence; human computer interaction; user modelling; adaptive systems in education
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Jose M. Alcaraz Calero

E-Mail Website
Guest Editor

School of Computing, Engineering and Physical Sciences, University of the West of Scotland (UWS), High Street, Paisley PA1 2BE, UK
Interests: AI models for recommender systems; trust reputation for smart networks and services; cognitive networks and services

Prof. Dr. David Martín Gómez

E-Mail Website
Guest Editor

Intelligent Systems Lab, Universidad Carlos III de Madrid, Calle Butarque 15, Leganés, 28911 Madrid, Spain
Interests: real-time perception systems; computer vision; sensor fusion; autonomous ground vehicles; unmanned aerial vehicles; navigation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With an ever-widening range of areas where recommender systems are successfully proving their applicability, having the opportunity to gather the latest advances in recommender systems and data mining will give us the opportunity to have a complete platform to present and discuss the latest developments in this field. The following points can be highlighted:

Focus on cutting-edge research: This Special Issue will showcase the latest and most innovative research in the field, making it an ideal platform for authors to share their work and ideas.
High visibility: This Special Issue will be widely disseminated, providing authors with a high-visibility platform to reach a broad and influential audience.
Interdisciplinary nature: Recommender systems and data mining are interdisciplinary fields that bring together researchers from a variety of backgrounds. This Special Issue will provide an opportunity for these researchers to collaborate and share their knowledge.
Relevance: Recommender systems and data mining play a critical role in many real-world applications and are of great interest to researchers, practitioners, and policymakers. The Special Issue will provide valuable insights into this rapidly evolving field.
Expert review: All submissions will undergo a rigorous review process, ensuring that only the highest-quality research is accepted for publication.

By submitting to this Special Issue, authors will have the opportunity to make a significant contribution to the advancement of the field, while also raising their own profile and that of their research.

This Special Issue aims to cover the recent emerging trends and applications for Recommender Systems and Data Mining. Topics of interest include, but are not limited to:

Context-aware Recommender Systems;
Affective Recommender Systems;
Trustworthy Recommender Systems;
Intelligent Recommender Systems in User-centred Scenarios (e.g., medicine, education, transportation, etc.);
Human Decision Making in Recommender Systems;
Implicit versus Explicit Feedback in Recommender Systems;
Content-based Recommender Systems;
Context-aware Recommender Systems;
Personalization in Recommender Systems;
Deep learning/reinforcement learning/federated learning in Recommender Systems;
Meta-learning in Recommender Systems;
Hybrid Recommender Systems;
Recommender System Evaluation and Metrics;
Scalability and Performance of Recommender Systems;
Explanation and Interpretability of Recommender Systems;
Ethical and Privacy Issues in Recommender Systems.

Prof. Dr. Jesús G. Boticario
Prof. Dr. Jose M. Alcaraz Calero
Prof. Dr. David Martín Gómez
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

recommender systems
data mining
deep learning
user modelling
multimodal data processing
collaborative filtering
content-based filtering
cold start
context information processing
personalization and ambient intelligence
decision biases
ethics in data mining

Published Papers (11 papers)

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Research

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22 pages, 6689 KiB

Open AccessArticle

An Actor-Critic Hierarchical Reinforcement Learning Model for Course Recommendation

by Kun Liang, Guoqiang Zhang, Jinhui Guo and Wentao Li

Electronics 2023, 12(24), 4939; https://doi.org/10.3390/electronics12244939 - 8 Dec 2023

Viewed by 1104

Abstract

Online learning platforms provide diverse course resources, but this often results in the issue of information overload. Learners always want to learn courses that are appropriate for their knowledge level and preferences quickly and accurately. Effective course recommendation plays a key role in helping learners select appropriate courses and improving the efficiency of online learning. However, when a user is enrolled in multiple courses, existing course recommendation methods face the challenge of accurately recommending the target course that is most relevant to the user because of the noise courses. In this paper, we propose a novel reinforcement learning model named Actor-Critic Hierarchical Reinforcement Learning (ACHRL). The model incorporates the actor-critic method to construct the profile reviser. This can remove noise courses and make personalized course recommendations effectively. Furthermore, we propose a policy gradient based on the temporal difference error to reduce the variance in the training process, to speed up the convergence of the model, and to improve the accuracy of the recommendation. We evaluate the proposed model using two real datasets, and the experimental results show that the proposed model significantly outperforms the existing recommendation models (improving 3.77% to 13.66% in terms of HR@5). Full article

(This article belongs to the Special Issue Recommender Systems and Data Mining)

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24 pages, 3609 KiB

Open AccessArticle

Point-of-Interest Recommendations Based on Immediate User Preferences and Contextual Influences

by Jingwen Li, Yi Yang, Xu Gong, Jianwu Jiang, Yanling Lu, Jinjin Lu and Shaoshao Xie

Electronics 2023, 12(20), 4199; https://doi.org/10.3390/electronics12204199 - 10 Oct 2023

Viewed by 945

Abstract

With the development of various location-based social networks (LSBNs), personalized point-of-interest (POI) recommendations have become a recent research hotspot. Current recommendation methods tend to mine user preferences from their historical check-in records but overlook interest deviations caused by real-time geographic environments and immediate interests present in the records, failing to meet users’ real-time and accurate needs. Therefore, this paper proposes a composite preference-based recommendation model (CPRM) for personalized POI recommendation. This method first extracts multi-factor contextual features, constructs a dual-layer attention network (DLAN) to capture long and short-term preferences, combines real-time geographic scenarios to uncover user immediate preferences, and then weights and fuses these three types of preferences to generate user composite preferences. Finally, a prediction function is employed to obtain the Top-N recommendation list. The experiments on two classic datasets, Foursquare and Gowalla, affirm the effectiveness of the model presented in this paper and offer a novel approach for providing personalized POI recommendations to users. Full article

(This article belongs to the Special Issue Recommender Systems and Data Mining)

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0 pages, 4862 KiB

Open AccessArticle

RETRACTED: A Global Structural Hypergraph Convolutional Model for Bundle Recommendation

by Xingtong Liu and Man Yuan

Electronics 2023, 12(18), 3952; https://doi.org/10.3390/electronics12183952 - 19 Sep 2023

Cited by 1 | Viewed by 1070 | Retraction

Abstract

Bundle recommendations provide personalized suggestions to users by combining related items into bundles, aiming to enhance users’ shopping experiences and boost merchants’ sales revenue. Existing solutions based on graph neural networks (GNN) face several significant challenges: (1) it is demanding to explicitly model multiple complex associations using standard graph neural networks, (2) numerous additional nodes and edges are introduced to approximate higher-order associations, and (3) the user–bundle historical interaction data are highly sparse. In this work, we propose a global structural hypergraph convolutional model for bundle recommendation (SHCBR) to address the above problems. Specifically, we jointly incorporate multiple complex interactions between users, items, and bundles into a relational hypergraph without introducing additional nodes and edges. The hypergraph structure inherently incorporates higher-order associations, thereby alleviating the training burden on neural networks and the dilemma of scarce data effectively. In addition, we design a special matrix propagation rule that captures non-pairwise complex relationships between entities. Using item nodes as links, structural hypergraph convolutional networks learn representations of users and bundles on a relational hypergraph. Experiments conducted on two real-world datasets demonstrate that the SHCBR outperforms the state-of-the-art baselines by 11.07–25.66% on Recall and 16.81–33.53% on NDCG. Experimental results further indicate that the approach based on hypergraphs can offer new insights for addressing bundle recommendation challenges. The codes and datasets have been publicly released on GitHub. Full article

(This article belongs to the Special Issue Recommender Systems and Data Mining)

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17 pages, 3187 KiB

Open AccessArticle

A Multimodal User-Adaptive Recommender System

by Nicolás Torres

Electronics 2023, 12(17), 3709; https://doi.org/10.3390/electronics12173709 - 2 Sep 2023

Viewed by 1852

Abstract

Traditional recommendation systems have predominantly relied on user-provided ratings as explicit input. Concurrently, visually aware recommender systems harness inherent visual cues within data to decode item characteristics and deduce user preferences. However, the untapped potential of incorporating item images into the recommendation process warrants investigation. This paper introduces an original convolutional neural network (CNN) architecture that leverages multimodal information, connecting user ratings with product images to enhance item recommendations. A central innovation of the proposed model is the User-Adaptive Filtering Module, a dynamic component that utilizes user profiles to generate personalized filters. Through meticulous visual influence analysis, the effectiveness of these filters is demonstrated. Furthermore, experimental results underscore the competitive performance of the approach compared to traditional collaborative filtering methods, thereby offering a promising avenue for personalized recommendations. This approach capitalizes on user adaptation patterns, enhancing the understanding of user preferences and visual attributes. Full article

(This article belongs to the Special Issue Recommender Systems and Data Mining)

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19 pages, 3538 KiB

Open AccessArticle

A Privacy-Preserving Time-Aware Method for Next POI Recommendation

by Jianyong Fan, Chenxi Pan, Yue Geng and Shuyu Li

Electronics 2023, 12(15), 3208; https://doi.org/10.3390/electronics12153208 - 25 Jul 2023

Cited by 2 | Viewed by 922

Abstract

Compared with traditional point-of-interest (POI) recommendation, next POI recommendation is more difficult and requires comprehensive consideration of users’ behavior patterns, spatial–temporal context, and other information. In addition, unreliable service providers may disclose the privacy of users when providing recommendation services. For next POI recommendation, a privacy-preserving time-aware recommendation method (PPTA-RM) is proposed in this paper. The PPTA-RM method is based on centralized differential privacy and combines coarse-grained recommendation with fine-grained recommendation. At the coarse-grained level, the users’ POI category preference is modeled by improved matrix factorization and predicted by singular spectrum analysis (SSA), and gradient perturbation is carried out during the matrix factorization process to protect the POI category preference of users. At the fine-grained level, the users’ POI preference is modeled and predicted by an improved hyperlink-induced topic search (HITS) algorithm, which considers the weighted combination of users’ social attributes and POI geographic distance attributes, and a privacy budget allocation strategy considering the visit count of POIs is designed to add Laplace noise to the check-in data of users. The experimental analysis on two real datasets shows that the proposed method improves F1-Score@10 by 0.4–21.8% under different privacy budgets, which validates that the proposed method holds the next POI recommendation accuracy better while preserving the user’s privacy. Full article

(This article belongs to the Special Issue Recommender Systems and Data Mining)

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16 pages, 925 KiB

Open AccessArticle

A Two-Path Multibehavior Model of User Interaction

by Mingyue Qu, Nan Wang and Jinbao Li

Electronics 2023, 12(14), 3048; https://doi.org/10.3390/electronics12143048 - 12 Jul 2023

Viewed by 859

Abstract

Personalized recommendation is an important part of e-commerce platforms. In recommendation systems, a neural network is used to enhance collaborative filtering to accurately capture user preferences, so as to obtain better recommendation performance. Traditional recommendation methods focus on the results of a single user behavior, ignoring the modeling of multiple interaction behaviors of users, such as click, add to cart and purchase. Although many studies have also focused on multibehavior modeling, two important challenges remain: (1) Since the multiple behaviors of the time-evolving trends of context information are ignored, it is still a challenge to identify the multimodal relationships of behaviors; (2) surveillance signals are still sparse. In order to solve these problem, this paper proposes a two-path multibehavior model of user interaction (TP_MB). First, a two-path learning strategy is introduced to maximize the multiple-interaction information of users and items learned by the two paths, which effectively enhances the robustness of the model. Second, a multibehavior dependent encoder is designed. Contextual information is obtained through behavior dependencies in the interaction of different users. In addition, three contrastive learning methods are designed, which not only obtain additional auxiliary supervision signals, but also alleviate the problem of sparse supervision signals. Extensive experiments on two real datasets demonstrate that our method outperforms state-of-the-art multibehavior recommendation methods. Full article

(This article belongs to the Special Issue Recommender Systems and Data Mining)

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15 pages, 973 KiB

Open AccessArticle

Recommender System for Arabic Content Using Sentiment Analysis of User Reviews

by Amani Al-Ajlan and Nada Alshareef

Electronics 2023, 12(13), 2785; https://doi.org/10.3390/electronics12132785 - 23 Jun 2023

Cited by 2 | Viewed by 1381

Abstract

Recommender systems are used as effective information-filtering techniques to automatically predict and identify sets of interesting items for users based on their preferences. Recently, there have been increasing efforts to use sentiment analysis of user reviews to improve the recommendations of recommender systems. Previous studies show the advantage of integrating sentiment analysis with recommender systems to enhance the quality of recommendations and user experience. However, limited research has been focused on recommender systems for Arabic content. This study, therefore, sets out to improve Arabic recommendation systems and investigate the impact of using sentiment analysis of user reviews on the quality of recommendations. We propose two collaborative filtering recommender systems for Arabic content: the first depends on users’ ratings, and the second uses sentiment analysis of users’ reviews to enhance the recommendations. These proposed models were tested using the Large-Scale Arabic Book Reviews dataset. Our results show that, when the user review sentiment analysis is combined with recommender systems, the quality of the recommendations is improved. The best model was the singular value decomposition (SVD) with the Arabic BERT–mini model, which yielded minimum errors in terms of RMSE and MAE values and outperformed the performance of other previous studies in the literature. Full article

(This article belongs to the Special Issue Recommender Systems and Data Mining)

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15 pages, 1118 KiB

Open AccessArticle

A Code Reviewer Recommendation Approach Based on Attentive Neighbor Embedding Propagation

by Jiahui Liu, Ansheng Deng, Qiuju Xie and Guanli Yue

Electronics 2023, 12(9), 2113; https://doi.org/10.3390/electronics12092113 - 5 May 2023

Viewed by 990

Abstract

Code review as an effective software quality assurance practice has been widely applied in many open-source software communities. However, finding a suitable reviewer for certain codes can be very challenging in open-source communities due to the difficulty of learning the characteristics of reviewers and the code-reviewer interaction sparsity in open-source software communities. To tackle this problem, most previous approaches focus on learning developers’ capabilities and experiences and recommending suitable developers based on their historical interactions. However, such approaches usually suffer from data-sparsity and noise problems, which may reduce the recommendation accuracy. In this paper, we propose an attentive neighbor embedding propagation enhanced code reviewer recommendation framework (termed ANEP). In ANEP, we first construct the reviewer–code interaction graph and learn the semantic representations of the reviewer and code based on the transformer model. Then, we explicitly explore the attentive high-order embedding propagation of reviewers and code and refine the representations along their neighbors. Finally, to evaluate the effectiveness of ANEP, we conduct extensive experiments on four real-world datasets. The experimental results show that ANEP outperforms other state-of-the-art approaches significantly. Full article

(This article belongs to the Special Issue Recommender Systems and Data Mining)

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19 pages, 1394 KiB

Open AccessArticle

Exploring Behavior Patterns for Next-POI Recommendation via Graph Self-Supervised Learning

by Daocheng Wang, Chao Chen, Chong Di and Minglei Shu

Electronics 2023, 12(8), 1939; https://doi.org/10.3390/electronics12081939 - 20 Apr 2023

Cited by 1 | Viewed by 1542

Abstract

Next-point-of-interest (POI) recommendation is a crucial part of location-based social applications. Existing works have attempted to learn behavior representation through a sequence model combined with spatial-temporal-interval context. However, these approaches ignore the impact of implicit behavior patterns contained in the visit trajectory on user decision making. In this paper, we propose a novel graph self-supervised behavior pattern learning model (GSBPL) for the next-POI recommendation. GSBPL applies two graph data augmentation operations to generate augmented trajectory graphs to model implicit behavior patterns. At the same time, a graph preference representation encoder (GPRE) based on geographical and social context is proposed to learn the high-order representations of trajectory graphs, and then capture implicit behavior patterns through contrastive learning. In addition, we propose a self-attention based on multi-feature embedding to learn users’ short-term dynamic preferences, and finally combine trajectory graph representation to predict the next location. The experimental results on three real-world datasets demonstrate that GSBPL outperforms the supervised learning baseline in terms of performance under the same conditions. Full article

(This article belongs to the Special Issue Recommender Systems and Data Mining)

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17 pages, 3587 KiB

Open AccessArticle

A Knowledge Concept Recommendation Model Based on Tensor Decomposition and Transformer Reordering

by Zhaoyu Shou, Yishuai Chen, Hui Wen, Jinghua Liu, Jianwen Mo and Huibing Zhang

Electronics 2023, 12(7), 1593; https://doi.org/10.3390/electronics12071593 - 28 Mar 2023

Viewed by 1316

Abstract

To help students choose the knowledge concepts that meet their needs so that they can learn courses in a more personalized way, thus improving the effectiveness of online learning, this paper proposes a knowledge concept recommendation model based on tensor decomposition and transformer reordering. Firstly, the student tensor, knowledge concept tensor, and interaction tensor are created based on the heterogeneous data of the online learning platform are fused and simplified as an integrated tensor; secondly, we perform multi-dimensional comprehensive analysis on the integrated tensor with tensor-based high-order singular value decomposition to obtain the student personalized feature matrix and the initial recommendation sequence of knowledge concepts, and then obtain the latent embedding matrix of knowledge concepts via Transformer that combine initial recommendation sequence of knowledge concepts and knowledge concept learning sequential information; finally, the final Top-N knowledge concept recommendation list is generated by fusing the latent embedding matrix of knowledge concepts and the students’ personalized feature matrix. Experiments on two real datasets show that the model recommendation performance of this paper is better compared to the baseline model. Full article

(This article belongs to the Special Issue Recommender Systems and Data Mining)

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Review

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24 pages, 1045 KiB

Open AccessReview

A Survey on Recommendation Methods Based on Social Relationships

by Rui Chen, Kangning Pang, Min Huang, Hui Liang, Shizheng Zhang, Lei Zhang, Pu Li, Zhengwei Xia, Jianwei Zhang and Xiangjie Kong

Electronics 2023, 12(22), 4564; https://doi.org/10.3390/electronics12224564 - 7 Nov 2023

Cited by 1 | Viewed by 1029

Abstract

With the rapid development of online social networks recently, more and more online users have participated in social network activities and rich social relationships are formed accordingly. These social relationships provide a rich data source and research basis for in-depth study on recommender systems (RSs), while also promoting the development of RSs based on social networks. To solve the problems of cold start and sparsity in RSs, many recommendation algorithms are constantly being proposed. Motivated by the availability of rich social connections in today’s RSs, a large number of recommendation techniques based on social relationships have been proposed recently, achieving good recommendation results, and have become the mainstream research direction in the field of RSs, attracting more and more researchers to engage in this research. In this study, we mainly review and summarize the social relationship-based recommendation methods and techniques in RSs, and study some recent deep social relationship recommendation methods and techniques based on deep learning (DL), including the latest social matrix factorization (MF)-based recommendation methods and graph neural network (GNN)-based recommendation methods. Finally, we discuss the potential impact that may improve the RS and future direction. In this article, we aim to introduce the recent recommendation techniques integrating social relationships to solve data sparsity and cold start, and provide a new perspective for improving the performance of RSs, thereby providing useful resources in the state-of-the-art research results for future researchers. Full article

(This article belongs to the Special Issue Recommender Systems and Data Mining)

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