1. Hibridni priporočilni sistem za izposojo knjig v sistemu COBISS : magistrsko deloRene Svenšek, 2025, master's thesis Abstract: V magistrskem delu obravnavamo razvoj in primerjavo različnih pristopov priporočilnih sistemov na področju knjižničnih sistemov. Najprej so predstavljeni teoretična izhodišča, uporabljene tehnologije in obdelava podatkov, ki so služili kot podlaga za implementacijo. V nadaljevanju so razviti in analizirani štirje pristopi: sodelovalno filtriranje, vsebinsko filtriranje ter mešani in kaskadni hibridni pristop. Eksperimentalni del temelji na vrednotenju z uporabo metrik HR@K, MRR@K in NDCG@K. Rezultati kažejo, da hibridni pristopi presegajo osnovne metode. Med njimi se je kot najbolj učinkovit izkazal kaskadni hibridni model, ki najbolje združuje prednosti obeh osnovnih tehnik in se prilagaja značilnostim knjižničnih podatkov.
Keywords: hibridni priporočilni sistem, knjižnični sistem, sodelovalno filtriranje, vsebinsko filtriranje, obdelava naravnega jezika
Published in DKUM: 17.10.2025; Views: 0; Downloads: 19
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2. Rethinking English studies through AI : challenges, ethics, and innovationTomaž Onič, Mladen Borovič, David Hazemali, 2025, other scientific articles Keywords: chatbots, artificial intelligence, generative artificial intelligence, large language models, English studies
Published in DKUM: 27.08.2025; Views: 0; Downloads: 6
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3. Evaluating Proprietary and Open-Weight Large Language Models as Universal Decimal Classification Recommender SystemsMladen Borovič, Eftimije Tomovski, Tom Li Dobnik, Sandi Majninger, 2025, original scientific article Abstract: Manual assignment of Universal Decimal Classification (UDC) codes is time-consuming and inconsistent as digital library collections expand. This study evaluates 17 large language models (LLMs) as UDC classification recommender systems, including ChatGPT variants (GPT-3.5, GPT-4o, and o1-mini), Claude models (3-Haiku and 3.5-Haiku), Gemini series (1.0-Pro, 1.5-Flash, and 2.0-Flash), and Llama, Gemma, Mixtral, and DeepSeek architectures. Models were evaluated zero-shot on 900 English and Slovenian academic theses manually classified by professional librarians. Classification prompts utilized the RISEN framework, with evaluation using Levenshtein and Jaro–Winkler similarity, and a novel adjusted hierarchical similarity metric capturing UDC’s faceted structure. Proprietary systems consistently outperformed open-weight alternatives by 5–10% across metrics. GPT-4o achieved the highest hierarchical alignment, while open-weight models showed progressive improvements but remained behind commercial systems. Performance was comparable between languages, demonstrating robust multilingual capabilities. The results indicate that LLM-powered recommender systems can enhance library classification workflows. Future research incorporating fine-tuning and retrieval-augmented approaches may enable fully automated, high-precision UDC assignment systems.
Keywords: universal decimal classification, large language models, conversational systems, recommender systems, prompt engineering, zero-shot classification, hierarchical similarity
Published in DKUM: 21.07.2025; Views: 0; Downloads: 11
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4. Evaluating chatbot assistance in historical document analysisDavid Hazemali, Janez Osojnik, Tomaž Onič, Tadej Todorović, Mladen Borovič, 2024, original scientific article Abstract: The article explores the potential of PDFGear Copilot, a chatbot-based PDF editing tool, in assisting with the analysis of historical documents. We evaluated the chatbot's performance on a document relating to the Slovenian War of Independence. We included 25 factual and 5 interpretative questions to address its formal characteristics and content details, assess its capacity for in-depth interpretation and contextualized critical analysis, and evaluate the chatbot’s language use and robustness. The chatbot exhibited some ability to answer factual questions, even though its performance varied. It demonstrated proficiency in navigating document structure, named entity recognition, and extracting basic document information. However, performance declined significantly in tasks such as document type identification, content details, and tasks requiring deeper text analysis. For interpretative questions, the chatbot's performance was notably inadequate, failing to link cause-and-effect relationships and provide the depth and nuance required for historical inquiries.
Keywords: chatbots, historical document analyses, Yugoslavia, wars, generative, artificial intelligence, large language models
Published in DKUM: 18.07.2025; Views: 0; Downloads: 10
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8. The OpenScience Slovenia metadata datasetMladen Borovič, Marko Ferme, Janez Brezovnik, Sandi Majninger, Albin Bregant, Goran Hrovat, Milan Ojsteršek, 2020, other scientific articles Abstract: The OpenScience Slovenia metadata dataset contains metadata entries for Slovenian public domain academic documents which include undergraduate and postgraduate theses, research and professional articles, along with other academic document types. The data within the dataset was collected as a part of the establishment of the Slovenian Open-Access Infrastructure which defined a unified document collection process and cataloguing for universities in Slovenia within the infrastructure repositories. The data was collected from several already established but separate library systems in Slovenia and merged into a single metadata scheme using metadata deduplication and merging techniques. It consists of text and numerical fields, representing attributes that describe documents. These attributes include document titles, keywords, abstracts, typologies, authors, issue years and other identifiers such as URL and UDC. The potential of this dataset lies especially in text mining and text classification tasks and can also be used in development or benchmarking of content-based recommender systems on real-world data.
Keywords: metadata, real world data, text data, text mining, text identification, natural language processing
Published in DKUM: 22.05.2025; Views: 0; Downloads: 8
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9. Prompt engineering for chatbotsMladen Borovič, 2025, other educational material Abstract: The learning material describes the remarkable and rapid progress in artificial intelligence conversational systems, from simple to advanced ones that now handle complex tasks and are becoming indispensable in various industries. It emphasises that the systems are quickly improving in understanding context and providing accurate answers, which is changing how technology is used. Because of this, the author introduces the concept of prompt engineering as a key skill for effective communication with these systems and obtaining the best results. The material serves as a practical guide to mastering this skill, although the author acknowledges that further development may make this knowledge unnecessary in the future. Nevertheless, the importance of clear communication remains, as conversational systems are just an extension of our need for it in the digital world.
Keywords: artificial intelligence, conversational systems, prompt engineering
Published in DKUM: 06.05.2025; Views: 0; Downloads: 23
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10. Oblikovanje ukazov pri uporabi pogovornih sistemovMladen Borovič, 2025, other educational material Abstract: Učno gradivo opisuje izjemen in hiter napredek pogovornih sistemov umetne inteligence, od preprostih do naprednih, ki danes obravnavajo kompleksne naloge in postajajo nepogrešljivi v različnih panogah. Poudarja, da se sistemi hitro izboljšujejo v razumevanju konteksta in zagotavljanju natančnih odgovorov, kar spreminja način uporabe tehnologije. Zaradi tega avtor uvaja pojem oblikovanja ukazov (prompt engineering) kot ključne veščine za učinkovito komunikacijo s temi sistemi in pridobivanje najboljših rezultatov. Gradivo služi kot praktičen vodič za obvladovanje te veščine, čeprav avtor priznava, da bo nadaljnji razvoj morda to znanje v prihodnosti naredil nepotrebno. Kljub temu ostaja pomembnost jasne komunikacije, saj so pogovorni sistemi le razširitev naše potrebe po njej v digitalnem svetu.
Keywords: umetna inteligenca, pogovorni sistemi, oblikovanje ukazov
Published in DKUM: 06.05.2025; Views: 0; Downloads: 9
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