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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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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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Zihang Xu and Chiawei Chu
Ensuring the sustainability of transportation infrastructure for electric vehicles (e-trans) is increasingly imperative in the pursuit of decarbonization goals and addressing the pressing energy shortage. By prioritizing the development and maintenance o...
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Zhengbao Li, Jianfeng Dai, Yuanxin Luan, Nan Sun and Libin Du
Human marine activities are becoming increasingly frequent. The adverse marine environment has led to an increase in man overboard incidents, resulting in significant losses of life and property. After a drowning accident, the accurate location informati...
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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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Zheng Li, Xueyuan Huang, Liupeng Gong, Ke Yuan and Chun Liu
Next Point-of-Interest (POI) recommendation has shown great value for both users and providers in location-based services. Existing methods mainly rely on partial information in users? check-in sequences, and are brittle to users with few interactions. M...
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Sumet Darapisut, Komate Amphawan, Nutthanon Leelathakul and Sunisa Rimcharoen
Location-based recommender systems (LBRSs) have exhibited significant potential in providing personalized recommendations based on the user?s geographic location and contextual factors such as time, personal preference, and location categories. However, ...
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Loris Barillari, Augusto Della Torre, Gianluca Montenegro and Angelo Onorati
In the last decade, additive manufacturing (AM) techniques have been progressively applied to the manufacturing of many mechanical components. Compared to traditional techniques, this technology is characterized by disruptive potential in terms of the co...
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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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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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