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Applied Machine Learning for Information Retrieval
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Applied Machine Learning for Information Retrieval

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

Deadline for manuscript submissions: 30 September 2025 | Viewed by 2350

Special Issue Editors

Dr. Gianni Costa

E-Mail Website
Guest Editor
Institute of High Performance Computing and Networks (ICAR) of the National Research Council of Italy (CNR), 87036 Rende, Italy
Interests: data mining; machine learning; recommender systems; social network analysis; text mining; semi-structured data analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Riccardo Ortale

E-Mail Website
Guest Editor
Institute of High Performance Computing and Networks (ICAR) of the National Research Council of Italy (CNR), 87036 Rende, Italy
Interests: data mining; machine learning; recommender systems; social network/media analysis; text mining; semistructured data analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Searching through, organizing, and interpreting information has advanced significantly due to machine learning and information retrieval integration. Given the exponential growth of digital content, applying machine learning approaches to information retrieval is crucial for improving search relevancy, user experience, and data management. This Special Issue will present approaches and techniques that address current issues and pave the way for future research, with the goal being to showcase machine learning research and practical applications in the field of information retrieval.

We welcome submissions demonstrating how machine learning is beneficially utilized in different fields of information retrieval, such as search engines, recommendation systems, natural language processing, and multimedia retrieval. This Special Issue's topics of interest encompass, but are not restricted to, the following:

  • Search and ranking algorithms
  • Recommendation systems
  • Natural language processing (NLP)
  • Image and video retrieval
  • User behavior modeling
  • Information extraction
  • Big data analytics
  • Security and privacy
  • Evaluation metrics and benchmarks
  • Large language models (LLMs)

Dr. Gianni Costa
Dr. Riccardo Ortale
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

  • search and ranking algorithms
  • recommendation systems
  • natural language processing (NLP)
  • image and video retrieval
  • user behavior modeling
  • information extraction
  • big data analytics
  • security and privacy
  • evaluation metrics and benchmarks
  • large language models (LLMs)

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Published Papers (2 papers)

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Research

19 pages, 2995 KiB  
Article
Enhanced Retrieval-Augmented Generation Using Low-Rank Adaptation
by Yein Choi, Sungwoo Kim, Yipene Cedric Francois Bassole and Yunsick Sung
Appl. Sci. 2025, 15(8), 4425; https://doi.org/10.3390/app15084425 - 17 Apr 2025
Viewed by 256
Abstract
Recent advancements in retrieval-augmented generation (RAG) have substantially enhanced the efficiency of information retrieval. However, traditional RAG-based systems still encounter challenges, such as high latency in output decision making, the inaccurate retrieval of road traffic-related laws and regulations, and considerable processing overhead in [...] Read more.
Recent advancements in retrieval-augmented generation (RAG) have substantially enhanced the efficiency of information retrieval. However, traditional RAG-based systems still encounter challenges, such as high latency in output decision making, the inaccurate retrieval of road traffic-related laws and regulations, and considerable processing overhead in large-scale searches. This study presents an innovative application of RAG technology for processing road traffic-related laws and regulations, particularly in the context of unmanned systems like autonomous driving. Our approach integrates embedding generation using a LoRA-enhanced BERT-based uncased model and an optimized retrieval strategy that combines maximal marginal similarity score thresholding with contextual compression retrieval. The proposed system enhances and achieves improved retrieval accuracy while reducing processing overhead. Leveraging road traffic-related regulatory datasets, the LoRA-enhanced model demonstrated remarkable performance gains over traditional RAG methods. Specifically, our model reduced the number of trainable parameters by 13.6% and lowered computational costs by 18.7%. Performance evaluations using BLEU, CIDEr, and SPICE scores revealed a 4.36% increase in BLEU-4, a 6.83% improvement in CIDEr, and a 5.46% improved in SPICE, confirming greater structural accuracy in regulatory text generation. Additionally, our method achieved an 8.5% improvement in retrieval accuracy across key metrics, outperforming baseline RAG systems. These contributions pave the way for more efficient and reliable traffic regulation processing, enabling better decision making in autonomous systems. Full article
(This article belongs to the Special Issue Applied Machine Learning for Information Retrieval)
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15 pages, 6590 KiB  
Article
The Analysis of Customers’ Transactions Based on POS and RFID Data Using Big Data Analytics Tools in the Retail Space of the Future
by Marina Kholod, Alberto Celani and Gianandrea Ciaramella
Appl. Sci. 2024, 14(24), 11567; https://doi.org/10.3390/app142411567 - 11 Dec 2024
Viewed by 1637
Abstract
In today’s business landscape, the volume of transaction data is rapidly increasing. This study explores the integration of Point of Sale (POS) and Radio-Frequency Identification (RFID) technologies to enhance the analysis of customer transactions using big data tools. By leveraging these technologies, businesses [...] Read more.
In today’s business landscape, the volume of transaction data is rapidly increasing. This study explores the integration of Point of Sale (POS) and Radio-Frequency Identification (RFID) technologies to enhance the analysis of customer transactions using big data tools. By leveraging these technologies, businesses can extract valuable insights to improve processes, optimize inventory, and boost customer satisfaction. The research employs an object—subject management approach, which facilitates real-time decision-making by merging retail transactions of the clients with their movement patterns. An experiment involving around 7000 customers demonstrates the effective collection and processing of POS and RFID data, highlighting the benefits of integrating these data streams. Key metrics, such as time spent in different store sections, provide deeper insights into consumer behavior. The findings reveal the potential of these technologies to transform retail services, offering opportunities for demand forecasting, risk management, and personalized customer experiences. The study concludes that merging POS and RFID data opens new avenues for developing management solutions aimed at enhancing customer engagement and the operational efficiency of the retailer. Future research will focus on further elaborating these solutions to maximize the benefits of integrated data analysis. Full article
(This article belongs to the Special Issue Applied Machine Learning for Information Retrieval)
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