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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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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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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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Wei Chen, Yihao Zhang, Yantuan Xian and Yonghua Wen
Tremendous academic articles face serious information overload problems while supporting literature searches. Finding a research article in a relevant domain that meets researchers? requirements is challenging. Hence, different paper recommendation model...
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Iosif Viktoratos and Athanasios Tsadiras
A domain that has gained popularity in the past few years is personalized advertisement. Researchers and developers collect user contextual attributes (e.g., location, time, history, etc.) and apply state-of-the-art algorithms to present relevant ads. A ...
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Kai Zhuang, Sen Wu and Shuaiqi Liu
Recommending loan products to applicants would benefit many financial businesses and individuals. Nevertheless, many loan products suffer from the cold start problem; i.e., there are no available historical data for training the recommendation model. Con...
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Xuhui Zeng, Shu Wang, Yunqiang Zhu, Mengfei Xu and Zhiqiang Zou
The recommendation system is one of the hotspots in the field of artificial intelligence that can be applied to recommend suitable ecological patterns for the countryside. Countryside ecological patterns mean advanced patterns that can be recommended to ...
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