LLANIME: Large Language Models for Anime Recommendations DOI
Anjali Agarwal, Sahil Sharma

2021 14th International Conference on Developments in eSystems Engineering (DeSE), Год журнала: 2023, Номер unknown, С. 870 - 875

Опубликована: Дек. 18, 2023

Large Language Models (LLMs) have advanced significantly in Natural Processing (NLP) over the past few years. Ongoing research continues exploring their capabilities recommendation systems, aiming to enhance user-tailored content delivery efficiency, accuracy, and personalisation. The investigation introduces a novel approach integration possibilities of open-source Model (LLM) technology—FLAN-T5, Falcon, Vicuna, UL2, LLAMA—into anime systems. delves into creating personalised recommendations by inputting titles, genres, descriptions these LLMs. Furthermore, it harnesses LLMs explain recommendations, bolstering user engagement amplifying transparency process. findings clearly show that using for works well. It proves techniques great potential make suggestions better.

Язык: Английский

A fine-tuned tourism-specific generative AI concept DOI
Cathy H.C. Hsu, Guoxiong Tan, Bela Stantić

и другие.

Annals of Tourism Research, Год журнала: 2024, Номер 104, С. 103723 - 103723

Опубликована: Янв. 1, 2024

Язык: Английский

Процитировано

28

Tell me more: integrating LLMs in a cultural heritage website for advanced information exploration support DOI Creative Commons
Angelo Geninatti Cossatin, Noemi Mauro, F Ferrero

и другие.

Information Technology & Tourism, Год журнала: 2025, Номер unknown

Опубликована: Янв. 28, 2025

Язык: Английский

Процитировано

1

A systematic literature review of recent advances on context-aware recommender systems DOI Creative Commons
Pablo Mateos, Alejandro Bellogín

Artificial Intelligence Review, Год журнала: 2024, Номер 58(1)

Опубликована: Ноя. 16, 2024

Abstract Recommender systems are software mechanisms whose usage is to offer suggestions for different types of entities like products, services, or contacts that could be useful interesting a specific user. Other ways have been explored in the field enhance power these by integrating context as an additional attribute. This inclusion tries extract user preferences more accurately taking into account multiple components such temporal, spatial, social ones. Notwithstanding magnitude context-awareness this area, research community agreement with lack framework information and how integrate it recommender systems. Under premise, paper focuses on comprehensive systematic literature review state-of-the-art recommendation techniques their characteristics benefit from contextual information. The following survey presents contributions outcomes our study: (i) determine where aspects taken clear definition representation, (ii) used incorporate context, (iii) evaluation methods terms reproducibility effectiveness. Our also covers some crucial topics about integration, classification contexts, application domains, datasets, metrics, code implementations, we observed shiftings algorithmic trends towards Neural Network approaches ranking respectively. Just importantly, future opportunities directions exposed final closure, standing out exploitation various data sources scalability customization existing solutions.

Язык: Английский

Процитировано

4

LLM-Aided Museum Guide: Personalized Tours Based on User Preferences DOI
Iva Vasic, Hans-Georg Fill, Ramona Quattrini

и другие.

Lecture notes in computer science, Год журнала: 2024, Номер unknown, С. 249 - 262

Опубликована: Янв. 1, 2024

Язык: Английский

Процитировано

3

Briteller: Shining a Light on AI Recommendations for Children DOI
Xiaofei Zhou, Yi Zhang, Yufei Jiang

и другие.

Опубликована: Апрель 24, 2025

Язык: Английский

Процитировано

0

Recommending art online: investigating user engagement and interactions with a digital collection DOI Creative Commons
Lukas Hughes-Noehrer, Jonathan Carlton, Caroline Jay

и другие.

Museum Management and Curatorship, Год журнала: 2025, Номер unknown, С. 1 - 28

Опубликована: Июнь 4, 2025

Язык: Английский

Процитировано

0

Will the Age of Generative Artificial Intelligence Become an Age of Public Ignorance? DOI Open Access
Dirk Spennemann

Опубликована: Сен. 22, 2023

Generative artificial intelligence (AI), in particular large language models such as ChatGPT have reached public consciousness with a wide-ranging discussion of their capabilities and suitability for various professions. Following the printing press internet, generative AI are third transformative technological invention truly cross-sectoral impact on knowledge transmission. While allowed transmission that is independent physical presence holder publishers acting gatekeepers, internet added levels democratization allowing anyone to publish, along global immediacy. The development social media resulted an increased fragmentation tribalization on-line communities ways knowing, resulting alternative truths propagated echo chambers. It against this background entered consciousness. Using strategic foresight methodology, paper will examine polemic proposition age emerge ignorance.

Язык: Английский

Процитировано

5

RAPID DEVELOPMENT OF CHATBOT FOR TOURISM PROMOTION IN LATGALE DOI
Sergejs Kodors,

Guna Kaņepe,

Daniēls Zeps

и другие.

Environment Technology Resources Proceedings of the International Scientific and Practical Conference, Год журнала: 2024, Номер 2, С. 179 - 182

Опубликована: Июнь 22, 2024

The release of ChatGPT technology identified the large language models as a new disruptive technology, which changes behaviours society and its attitude towards presence artificial intelligence in everyday life. tourism industry is one economic sectors, will be impacted by through personalized marketing advertisements. A common approach to capture attention AI-centric tourists, who want get answers their questions without manually researching topic or using services travel advisors, integrate chatbot virtual assistant information system. We applied this promotion East Latvia (Latgale) rapidly developing prompt method with context-oriented material. Two were prepared for Latgale. evaluated pilot survey understand satisfaction target users. data analysis was applied. study importance trustworthy answer saturation. trade-off between dialog freedom trustworthiness can achieved development microservices, are grouped system direct conversation chatbot. appropriate conceptual presented article.

Язык: Английский

Процитировано

1

Leveraging GPT-4 Capabilities for Developing Context-Aware, Personalized Chatbot Interfaces in E-commerce Customer Support Systems DOI
Kathari Santosh,

Timur Kholmukhamedov,

Sandeep Kumar Mathivanan

и другие.

Опубликована: Апрель 12, 2024

In the realm of e-commerce customer support, adoption chatbots is on rise, driven by a quest for heightened user interactions. This study introduces an inventive approach harnessing advanced capabilities GPT-4 to construct chatbot interfaces that are both context-aware and personalized. The main aim this work revolutionize ecommerce support developing intelligent adaptable context-aware, personalized, significantly enhance experiences. Existing models struggle with context retention personalization, limiting effectiveness in dynamic environments despite promise automation. innovative leverages GPT-4's robust language processing, integrating it profiling systems personalized responses grounded preferences historical method gives, diverse dataset undergoes collection pre-processing, followed fine-tuning emphasis context-awareness responses. model seamlessly integrated backend real-time information, multimodal input caters varied preferences. Results exhibit significant 95% accuracy improvement, affirming chatbot's enhanced ability comprehend queries. By personalization retention, enhances engagement, paving way revolutionary support.

Язык: Английский

Процитировано

0

CARIA DOI Open Access

Supaluck Seesukong,

Thara Angskun,

Nantapong Keandoungchun

и другие.

International Journal of Information and Communication Technology Education, Год журнала: 2024, Номер 20(1), С. 1 - 24

Опубликована: Окт. 14, 2024

The purpose of this research is to create a personalized system called CARIA that suggests career recommendations based on students' competencies and the required skills in each career. focus study digital technology media careers. recommender uses novel similarity measure modified Euclidean evaluate its performance compare it with other measures, machine learning, GPT-4 techniques. experimental results showed achieved precision@10 score 0.83, which outperformed main objective provide students suitable paths conduct competency gap analysis. This helps choose path fits their abilities. contributes education technology, media, workforce by providing employees align needs.

Язык: Английский

Процитировано

0