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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (30 August 2023) | Viewed by 14599

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Special Issue Editors

Dr. Konstantinos Pliakos

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Guest Editor

Department of Management, Strategy and Innovation, KU Leuven, 3000 Leuven, Belgium
Interests: recommender systems; machine learning; deep learning; AI for drug discovery; multi-target prediction; supervised and semi-supervised learning; dimensionality reduction

Dr. Alireza Gharahighehi

E-Mail Website
Guest Editor

Department of Public Health and Primary Care, KU Leuven, Campus KULAK, 8500 Kortrijk, Belgium
Interests: machine learning; recommender systems; beyond relevance evaluation; cold-start problem in recommender systems

Special Issue Information

Dear Colleagues,

In the era of digitalization and e-commerce, people use online platforms to find their desired products and services. Such platforms often accommodate enormous collections of entities; nevertheless, typically, each user is interested in only a tiny fraction of them. To this end, the role of personalized AI-driven recommender systems is paramount. Recommender systems (RSs) are based on intelligent models that leverage data mining and machine learning methodologies, learning users' preferences and recommending relevant items to each user. Typically, they manage to infer users' preferences by using historical user-item data as well as other types of available information, such as item and user side-information (i.e., features that describe the users/items in the system). RSs are omni-present as they are currently employed by movie and music platforms, online sellers, booking agencies, marketing agencies, and social media platforms.

This Special Issue is dedicated to new challenges and innovative approaches related to AI-driven recommender systems. We are pleased to invite submissions of original research on all aspects of recommendation, including the following topics:

bias and fairness in recommender systems
filter bubble problem
cold-start problem
multi-stakeholder recommendation
performance metrics and new aspects of evaluating recommendations
real-world implementations and scalability of recommendation algorithms
ethics around recommender systems
privacy and security
cross-domain and multi-modal recommendation
multimedia recommender systems (images, videos, music)
benchmarking and comparative studies

Dr. Konstantinos Pliakos
Dr. Alireza Gharahighehi
Guest Editors

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Keywords

recommender systems
machine learning
deep learning
AI

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Published Papers (5 papers)

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Research

12 pages, 943 KiB

Open AccessArticle

Developing a Convenience Store Product Recommendation System through Store-Based Collaborative Filtering

by Jaekyung Lee and Jinho Kim

Appl. Sci. 2023, 13(20), 11231; https://doi.org/10.3390/app132011231 - 12 Oct 2023

Cited by 2 | Viewed by 2414

Abstract

Providing personalized product recommendations in offline retail stores, especially small-format offline retail businesses such as convenience stores, poses a great challenge. To address this issue, this study aimed to find a solution by shifting the perspective on recommendation methods and altering the target of recommendations. In this study, recommending products was defined as suggesting products that should be introduced and displayed within the store. This recommendation system proposes products that individual stores have not yet introduced but are anticipated to be purchased by customers. Building upon this, we developed a store-based collaborative filtering recommendation system. Furthermore, various rules and logic pertinent to store operations and business considerations for convenience stores were integrated to implement this recommendation system. The accuracy and effectiveness of the system were demonstrated through its application in actual convenience stores. Results from the pilot implementation of the system showed that 88% of the newly recommended products in individual stores were sold within a week, and the sales revenue was 1.75 times higher than the average sales of those products across the entire stores. Survey results on business owners’ satisfaction yielded a score of 4.2 out of 5, indicating a high level of contentment. This research holds significance in extending the scope of personalized recommendation studies from primarily online platforms to offline retail businesses such as convenience stores. The study also suggests avenues for future research to address some of the identified limitations. Full article

(This article belongs to the Special Issue AI Methods for Recommender Systems)

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19 pages, 5538 KiB

Open AccessArticle

Attention-Based Personalized Compatibility Learning for Fashion Matching

by Xiaozhe Nie, Zhijie Xu, Jianqin Zhang and Yu Tian

Appl. Sci. 2023, 13(17), 9638; https://doi.org/10.3390/app13179638 - 25 Aug 2023

Viewed by 1571

Abstract

The fashion industry has a critical need for fashion compatibility. Modeling compatibility is a challenging task that involves extracting (in)compatible features of pairs, obtaining compatible relationships between matching items, and applying them to personalized recommendation tasks. Measuring compatibility is a complex and subjective concept in general. The complexity is reflected in the fact that relationships between fashion items are determined by multiple matching rules, such as color, shape, and material. Each personal aesthetic style and fashion preference differs, adding subjectivity to the compatibility concept. As a result, personalized factors must be considered. Previous works mainly utilize a convolutional neural network to measure compatibility by extracting general features, but they ignore fine-grained compatibility features and only model overall compatibility. We propose a novel neural network framework called the Attention-based Personalized Compatibility Embedding Network (PCE-Net). It comprises two components: attention-based compatibility embedding modeling and attention-based personal preference modeling. In the second part, we utilize matrix factorization and content-based features to obtain user preferences. Both pieces are jointly trained using the BPR framework in an end-to-end method. Extensive experiments on the IQON3000 dataset demonstrate that PCE-Net significantly outperforms most baseline methods. Full article

(This article belongs to the Special Issue AI Methods for Recommender Systems)

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17 pages, 2733 KiB

Open AccessArticle

FMGAN: A Filter-Enhanced MLP Debias Recommendation Model Based on Generative Adversarial Network

by Zhaoxuan Liu and Wenjie Luo

Appl. Sci. 2023, 13(13), 7975; https://doi.org/10.3390/app13137975 - 7 Jul 2023

Viewed by 1308

Abstract

In recommendation models, bias can distort the distribution of user-generated data, leading to inaccurate representation of user preferences. Failure to filter out biased data can result in significant learning errors, ultimately reducing the accuracy of the recommendation model. To address this issue, this paper proposes a Generative Adversarial Network (GAN) model comprising a filter-enhanced Multi-Layer Perceptron (MLP) generator and a linear discriminator to mitigate bias and improve the accuracy of the recommendation. The proposed model leverages the GAN architecture, where the filter structure in the generator enhances the data distribution before model training, allowing for the generation of more precise recommendation lists. The discriminator learns from the skew-corrected user review list to extract user features, which are then used alongside the recommendation list generated by G in an adversarial process. This adversarial process enables each component to optimize and improve itself while strengthening the correction effect. To enhance the accuracy of G generation, we evaluate the influence of three different input lists on the filter effect. Finally, we validate our model on two real-world datasets by comparing the effect of filter-augmented MLP and pure MLP generators. Our results demonstrate the effectiveness of filters, and our model achieves better recommendation accuracy than other baseline models. Full article

(This article belongs to the Special Issue AI Methods for Recommender Systems)

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20 pages, 4335 KiB

Open AccessArticle

Research on Hybrid Recommendation Model for Personalized Recommendation Scenarios

by Wenkai Ni, Yanhui Du, Xingbang Ma and Haibin Lv

Appl. Sci. 2023, 13(13), 7903; https://doi.org/10.3390/app13137903 - 5 Jul 2023

Cited by 2 | Viewed by 1809

Abstract

One of the five types of Internet information service recommendation technologies is the personalized recommendation algorithm, and knowledge graphs are frequently used in these algorithms. RippleNet is a personalized recommendation model based on knowledge graphs, but it is susceptible to localization issues in user portrait updating. In this study, we propose NRH (Node2vec-side and RippleNet Hybrid Model), a hybrid recommendation model based on RippleNet that uses Node2vec-side for item portrait modeling and explores potential association relationships of items; the user portrait is split into two parts, namely, a static history portrait and a dynamic preference portrait; the NRH model adopts a hybrid recommendation approach based on collaborative filtering and a knowledge graph to obtain the user’s preferences on three publicly accessible datasets; and comparison experiments with the mainstream model are lastly carried out. The AUC and ACC increased, respectively, by 0.9% to 29.5% and 1.6% to 31.4% in the MovieLens-1M dataset, by 1.5% to 17.1% and 4.4% to 18.7% in the Book-Crossing dataset, and by 0.8% to 27.9% and 2.9% to 24.1% in the Last.FM dataset. The RippleNet model was used for comparison experiments comparing suggestion diversity. According to the experimental findings, the NRH model performs better in accuracy and variety than the popular customized knowledge graph recommendation algorithms now in use. Full article

(This article belongs to the Special Issue AI Methods for Recommender Systems)

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18 pages, 1818 KiB

Open AccessArticle

A Personalized Learning Path Recommendation Method Incorporating Multi-Algorithm

by Yongjuan Ma, Lei Wang, Jiating Zhang, Fengjuan Liu and Qiaoyong Jiang

Appl. Sci. 2023, 13(10), 5946; https://doi.org/10.3390/app13105946 - 11 May 2023

Cited by 10 | Viewed by 6643

Abstract

In this era of intelligence, the learning methods of learners have substantially changed. Many learners choose to learn through online education platforms. Although learners may enjoy more high-quality educational resources, when they are faced with an abundance of resource information, they are prone to become lost in knowledge, among other problems. To solve this problem, a multi-algorithm collaborative, personalized, learning path recommendation model is proposed to provide learning guidance for learners of online learning platforms. First, the learner model is constructed from four perspectives: cognitive level, learning ability, learning style, and learning intensity. Second, the association rule algorithm is employed to generate a sequence of knowledge points and to plan the learning sequence of knowledge points for learners. Last, the swarm intelligence algorithm is utilized to ensure that each knowledge point is matched with personalized learning resources with a higher degree of adaptability so that learners can learn using a more targeted approach. The experimental results show that the research results of this paper can, to a certain extent, recommend ideal learning paths to target users, effectively improve the accuracy of recommended resources, and thus improve the learning quality and learning effect of users. Full article

(This article belongs to the Special Issue AI Methods for Recommender Systems)

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