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Applied Sciences
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Recent Advances in Information Retrieval and Recommendation Systems
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Recent Advances in Information Retrieval and Recommendation Systems

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

Deadline for manuscript submissions: 20 April 2025 | Viewed by 5291

Special Issue Editors

Dr. Rosa Meo

E-Mail Website
Guest Editor
Department of Computer Science, University of Torino, C.so Svizzera 185, 10149 Torino, Italy
Interests: digital twins; sustainable agriculture; ML applied to smart agriculture; application of ML to law and information systems for specific domains; like tenders; public administrations; predictive maintenance
Dr. Luca Cagliero

E-Mail Website
Guest Editor
Department of Automation and Computer Science, Polytechnic of Turin, 10129 Turin, Italy
Interests: multidocument text summarization; cross-lingual text analytics; quantative trading systems based on ML; sentiment analysis; vector representations of text and deep natural Language processing; time series analysis and forecasting; anomaly detection from time series data; classification of structured data; itemset mining and association rule discovery; generalized pattern extraction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Data mining and machine learning have revolutionised many scientific fields. In information retrieval, systems can search the web, act as question-answering systems, work as personal assistants, work with chatbots, and search digital libraries.

Information retrieval systems can act as rankers, a typical task they share with recommendation systems. The two fields also share the ability to search efficiently and possibly in a personalised way in large corpora, knowledge bases, heterogeneous sources, content and digital libraries. Both compete in the same application areas. Both can advance with the integration of external knowledge, leading to knowledge-based systems.

Furthermore, the novel techniques of deep learning neural networks and transformers can advance both systems even more drastically, making them more similar and leading to convergence into a unique system type. 

This Special Issue addresses the above topics as well as the following topics:

  • The convergence of information retrieval and recommendation systems;
  • The architecture, the technology, the algorithms for searching, digesting, transforming, filtering, learning on massive data;
  • Real-time and online data processing and analysis;
  • Heterogeneous and multimedia content;
  • Pipelines and integration of machine learning tasks in the system;
  • Bias in data and its impact on system results;
  • Knowledge integration in the system;
  • Integration of context in question answering;
  • Personalisation and consideration of the user;
  • Privacy and robustness of the system;
  • Explainability of the system and its results;
  • Accountability of the pipeline;
  • Applications.

Dr. Rosa Meo
Dr. Luca Cagliero
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. Applied Sciences 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

  • recommendation systems
  • information retrieval
  • transformers
  • deep neural networks
  • bias
  • privacy preserving
  • accountability
  • knowledge integration
  • context aware
  • personalized system

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Published Papers (4 papers)

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Research

25 pages, 609 KiB  
Article
Emotion-Driven Music and IoT Devices for Collaborative Exer-Games
by Pedro Álvarez, Jorge García de Quirós and Javier Fabra
Appl. Sci. 2024, 14(22), 10251; https://doi.org/10.3390/app142210251 - 7 Nov 2024
Viewed by 892
Abstract
Exer-games are interactive experiences in which participants engage in physical exercises to achieve specific goals. Some of these games have a collaborative nature, wherein the actions and achievements of one participant produce immediate effects on the experiences of others. Music serves as a [...] Read more.
Exer-games are interactive experiences in which participants engage in physical exercises to achieve specific goals. Some of these games have a collaborative nature, wherein the actions and achievements of one participant produce immediate effects on the experiences of others. Music serves as a stimulus that can be integrated into these games to influence players’ emotions and, consequently, their actions. In this paper, a framework of music services designed to enhance collaborative exer-games is presented. These services provide the necessary functionality to generate personalized musical stimuli that regulate players’ affective states, induce changes in their physical performance, and improve the game experience. The solution requires to determine the emotions that each song may evoke in players. These emotions are considered when recommending the songs that are used as part of stimuli. Personalization seeds based on players’ listening histories are also integrated in the recommendations in order to foster the effects of those stimuli. Emotions and seeds are computed from the information available in Spotify data services, one of the most popular commercial music providers. Two small-scale experiments present promising preliminary results on how the players’ emotional responses match the affective information included in the musical elements of the solution. The added value of these affective services is that they are integrated into an ecosystem of Internet of Things (IoT) devices and cloud computing resources to support the development of a new generation of emotion-based exer-games. Full article
(This article belongs to the Special Issue Recent Advances in Information Retrieval and Recommendation Systems)
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17 pages, 1529 KiB  
Article
Perceived Usefulness of a Mandatory Information System
by Shimon Fridkin, Gil Greenstein, Avner Cohen and Aviran Damari
Appl. Sci. 2024, 14(16), 7413; https://doi.org/10.3390/app14167413 - 22 Aug 2024
Viewed by 927
Abstract
This study examines the adoption and implementation of an information system in a mandatory context focusing on an Israeli governmental organization. The system referred to as “Slot” is an online platform for managing educational activities within this organization. The research investigates the impact [...] Read more.
This study examines the adoption and implementation of an information system in a mandatory context focusing on an Israeli governmental organization. The system referred to as “Slot” is an online platform for managing educational activities within this organization. The research investigates the impact of the system on its functionality users and the results of its usage. Additionally, the study explores factors that influence the acceptance and utilization of information systems, including whether the willingness to use the system under instruction depends on other variables. The key findings of this study are: perceived ease of use significantly and positively influences perceived usefulness; perceived usefulness significantly and positively affects symbolic adoption; and supervisor influence significantly and positively impacts perceived usefulness. Moreover, the relationship between perceived ease of use and symbolic adoption is entirely mediated by perceived usefulness as is the relationship between supervisor influence and symbolic adoption. The study’s limitations include the relatively small sample size and the specific context of the research, which may limit the generalizability of the findings. Future research could explore similar models in different organizational settings to validate and extend the applicability of the results. The findings suggest that enhancing the perceived ease of use and usefulness of mandatory systems can significantly impact their symbolic adoption, with supervisory influence playing a crucial role in shaping user perceptions. These insights can inform strategies for more effective implementation and management of information systems in mandatory settings. Full article
(This article belongs to the Special Issue Recent Advances in Information Retrieval and Recommendation Systems)
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21 pages, 415 KiB  
Article
Distributed Action-Rule Discovery Based on Attribute Correlation and Vertical Data Partitioning
by Aileen C. Benedict and Zbigniew W. Ras
Appl. Sci. 2024, 14(3), 1270; https://doi.org/10.3390/app14031270 - 3 Feb 2024
Cited by 2 | Viewed by 1067
Abstract
The paper concerns the problem of action-rule extraction when datasets are large. Such rules can be used to construct a knowledge base in a recommendation system. One of the popular approaches to construct action rules in such cases is to partition the dataset [...] Read more.
The paper concerns the problem of action-rule extraction when datasets are large. Such rules can be used to construct a knowledge base in a recommendation system. One of the popular approaches to construct action rules in such cases is to partition the dataset horizontally (personalization) and vertically. Different clustering strategies can be used for this purpose. Action rules extracted from vertical clusters can be combined and used as knowledge discovered from the horizontal clusters of the initial dataset. The number of extracted rules strongly depends on the methods used to complete that task. In this study, we chose a software package called SCARI recently developed by Sikora and his colleagues. It follows a rule-based strategy for action-rule extraction that requires prior extraction of classification rules and generates a relatively small number of rules in comparison to object-based strategies, which discover action rules directly from datasets. Correlation between attributes was used to cluster them. We used an agglomerative strategy to cluster attributes of a dataset and present the results by using a dendrogram. Each level of the dendrogram shows a vertical partition schema for the initial dataset. From all partitions, for each level, action rules are extracted and then concatenated. Their precision, the lightness, and the number of rules are presented and compared. Lightness shows how many action rules can be applied on average for each tuple in a dataset. Full article
(This article belongs to the Special Issue Recent Advances in Information Retrieval and Recommendation Systems)
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12 pages, 2312 KiB  
Article
Research and Application of Edge Computing and Deep Learning in a Recommender System
by Xiaopei Hao, Xinghua Shan, Junfeng Zhang, Ge Meng and Lin Jiang
Appl. Sci. 2023, 13(23), 12541; https://doi.org/10.3390/app132312541 - 21 Nov 2023
Cited by 1 | Viewed by 1333
Abstract
Recommendation systems play a pivotal role in improving product competitiveness. Traditional recommendation models predominantly use centralized feature processing to operate, leading to issues such as excessive resource consumption and low real-time recommendation concurrency. This paper introduces a recommendation model founded on deep learning, [...] Read more.
Recommendation systems play a pivotal role in improving product competitiveness. Traditional recommendation models predominantly use centralized feature processing to operate, leading to issues such as excessive resource consumption and low real-time recommendation concurrency. This paper introduces a recommendation model founded on deep learning, incorporating edge computing and knowledge distillation to address these challenges. Recognizing the intricate relationship between the accuracy of deep learning algorithms and their complexity, our model employs knowledge distillation to compress deep learning. Teacher–student models were initially chosen and constructed in the cloud, focusing on developing structurally complex teacher models that incorporate passenger and production characteristics. The knowledge acquired from these models was then transferred to a student model, characterized by weaker learning capabilities and a simpler structure, facilitating the compression and acceleration of an intelligent ranking model. Following this, the student model underwent segmentation, and certain computational tasks were shifted to end devices, aligning with edge computing principles. This collaborative approach between the cloud and end devices enabled the realization of an intelligent ranking for product listings. Finally, a random selection of the passengers’ travel records from the last five years was taken to test the accuracy and performance of the proposed model, as well as to validate the intelligent ranking of the remaining tickets. The results indicate that, on the one hand, an intelligent recommendation system based on knowledge distillation and edge computing successfully achieved the concurrency and timeliness of the existing remaining ticket queries. Simultaneously, it guaranteed a certain level of accuracy, and reduced computing resource and traffic load on the cloud, showcasing its potential applicability in highly concurrent recommendation service scenarios. Full article
(This article belongs to the Special Issue Recent Advances in Information Retrieval and Recommendation Systems)
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