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Christos Troussas, Akrivi Krouska, Antonios Koliarakis and Cleo Sgouropoulou
Recommender systems are widely used in various fields, such as e-commerce, entertainment, and education, to provide personalized recommendations to users based on their preferences and/or behavior. ?his paper presents a novel approach to providing custom...
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Adrián Valera, Álvaro Lozano Murciego and María N. Moreno-García
Nowadays, recommender systems are present in multiple application domains, such as e-commerce, digital libraries, music streaming services, etc. In the music domain, these systems are especially useful, since users often like to listen to new songs and d...
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Camila Vaccari Sundermann, Marcos Aurélio Domingues, Roberta Akemi Sinoara, Ricardo Marcondes Marcacini and Solange Oliveira Rezende
Recommender systems help users by recommending items, such as products and services, that can be of interest to these users. Context-aware recommender systems have been widely investigated in both academia and industry because they can make recommendatio...
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Marco Polignano and Giovanni Semeraro
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Laila Esheiba, Amal Elgammal, Iman M. A. Helal and Mohamed E. El-Sharkawi
Manufacturers today compete to offer not only products, but products accompanied by services, which are referred to as product-service systems (PSSs). PSS mass customization is defined as the production of products and services to meet the needs of indiv...
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Anna I. Guseva,Vasiliy S. Kireev,Stanislav A. Filippov
Pág. 225 - 233
This article presents the study results of the business intelligence markets, the promote products on social media, and a new method for increasing the information pertinence in the scientific recommender systems, scientific information systems, analysis...
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Amin Beheshti, Shahpar Yakhchi, Salman Mousaeirad, Seyed Mohssen Ghafari, Srinivasa Reddy Goluguri and Mohammad Amin Edrisi
Intelligence is the ability to learn from experience and use domain experts? knowledge to adapt to new situations. In this context, an intelligent Recommender System should be able to learn from domain experts? knowledge and experience, as it is vital to...
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Luis M. de Campos, Juan M. Fernández-Luna, Juan F. Huete, Francisco J. Ribadas-Pena and Néstor Bolaños
In the context of academic expert finding, this paper investigates and compares the performance of information retrieval (IR) and machine learning (ML) methods, including deep learning, to approach the problem of identifying academic figures who are expe...
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Ichchha Pradeep Sharma, Tam V. Nguyen, Shruti Ajay Singh and Tom Ongwere
This paper focuses on addressing the complex healthcare needs of patients struggling with discordant chronic comorbidities (DCCs). Managing these patients within the current healthcare system often proves to be a challenging process, characterized by evo...
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Nikzad Chizari, Keywan Tajfar and María N. Moreno-García
In today?s technology-driven society, many decisions are made based on the results provided by machine learning algorithms. It is widely known that the models generated by such algorithms may present biases that lead to unfair decisions for some segments...
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Ken McGarry
In this work we combine sentiment analysis with graph theory to analyze user posts, likes/dislikes on a variety of social media to provide recommendations for YouTube videos. We focus on the topic of climate change/global warming, which has caused much a...
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Mouadh Guesmi, Mohamed Amine Chatti, Shoeb Joarder, Qurat Ul Ain, Clara Siepmann, Hoda Ghanbarzadeh and Rawaa Alatrash
Significant attention has been paid to enhancing recommender systems (RS) with explanation facilities to help users make informed decisions and increase trust in and satisfaction with an RS. Justification and transparency represent two crucial goals in e...
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Dharahas Tallapally, John Wang, Katerina Potika and Magdalini Eirinaki
Recommender systems have revolutionized the way users discover and engage with content. Moving beyond the collaborative filtering approach, most modern recommender systems leverage additional sources of information, such as context and social network dat...
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Fanny Jourdan, Titon Tshiongo Kaninku, Nicholas Asher, Jean-Michel Loubes and Laurent Risser
Automatic recommendation systems based on deep neural networks have become extremely popular during the last decade. Some of these systems can, however, be used in applications that are ranked as High Risk by the European Commission in the AI act?for ins...
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Georgios Chalkiadakis, Ioannis Ziogas, Michail Koutsmanis, Errikos Streviniotis, Costas Panagiotakis and Harris Papadakis
In this paper, we develop a novel hybrid recommender system for the tourism domain, which combines (a) a Bayesian preferences elicitation component which operates by asking the user to rate generic images (corresponding to generic types of POIs) in order...
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Antiopi Panteli and Basilis Boutsinas
Recommender systems aim to forecast users? rank, interests, and preferences in specific products and recommend them to a user for purchase. Collaborative filtering is the most popular approach, where the user?s past purchase behavior consists of the user...
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Xiaole Wang, Jiwei Qin, Shangju Deng and Wei Zeng
In recent years, the application of knowledge graphs to alleviate cold start and data sparsity problems of users and items in recommendation systems, has aroused great interest. In this paper, in order to address the insufficient representation of user a...
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Shahab Saquib Sohail, Asfia Aziz, Rashid Ali, Syed Hamid Hasan, Dag Øivind Madsen and M. Afshar Alam
In this paper, we propose an approach to recommender systems that incorporates human-centric aggregation via Ordered Weighted Aggregation (OWA) to prioritize the suggestions of expert rankers over the usual recommendations. We advocate for ranked recomme...
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Laura Plaza, Lourdes Araujo, Fernando López-Ostenero and Juan Martínez-Romo
Online learning is quickly becoming a popular choice instead of traditional education. One of its key advantages lies in the flexibility it offers, allowing individuals to tailor their learning experiences to their unique schedules and commitments. Moreo...
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Dionisis Margaris, Costas Vassilakis, Dimitris Spiliotopoulos and Stefanos Ougiaroglou
Collaborative filtering has proved to be one of the most popular and successful rating prediction techniques over the last few years. In collaborative filtering, each rating prediction, concerning a product or a service, is based on the rating values tha...
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