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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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Tamim Mahmud Al-Hasan, Aya Nabil Sayed, Faycal Bensaali, Yassine Himeur, Iraklis Varlamis and George Dimitrakopoulos
Recommender systems are a key technology for many applications, such as e-commerce, streaming media, and social media. Traditional recommender systems rely on collaborative filtering or content-based filtering to make recommendations. However, these appr...
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Diego Sánchez-Moreno, Vivian F. López Batista, María Dolores Muñoz Vicente, Ángel Luis Sánchez Lázaro and María N. Moreno-García
Information from social networks is currently being widely used in many application domains, although in the music recommendation area, its use is less common because of the limited availability of social data. However, most streaming platforms allow for...
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Albérico Travassos Rosário and Joana Carmo Dias
This study explores the transformative impact of IoT technologies on smart tourism, striving to boost operational efficiency and enrich the traveler experience. Using a systematic literature review with bibliometric analysis, we examined a sample of 83 s...
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Abdelghani Azri, Adil Haddi and Hakim Allali
Collaborative filtering (CF), a fundamental technique in personalized Recommender Systems, operates by leveraging user?item preference interactions. Matrix factorization remains one of the most prevalent CF-based methods. However, recent advancements in ...
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Deepanjal Shrestha, Tan Wenan, Deepmala Shrestha, Neesha Rajkarnikar and Seung-Ryul Jeong
This study introduces a data-driven and machine-learning approach to design a personalized tourist recommendation system for Nepal. It examines key tourist attributes, such as demographics, behaviors, preferences, and satisfaction, to develop four sub-mo...
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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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Ji-Yoon Kim and Chae-Kwan Lim
The electronic publication market is growing along with the electronic commerce market. Electronic publishing companies use recommendation systems to increase sales to recommend various services to consumers. However, due to data sparsity, the recommenda...
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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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