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Ming-Yen Lin, Ping-Chun Wu and Sue-Chen Hsueh
This study introduces session-aware recommendation models, leveraging GRU (Gated Recurrent Unit) and attention mechanisms for advanced latent interaction data integration. A primary advancement is enhancing latent context, a critical factor for boosting ...
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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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Leyla Gamidullaeva, Alexey Finogeev, Mikhail Kataev and Larisa Bulysheva
Despite of tourism infrastructure and software, the development of tourism is hampered due to the lack of information support, which encapsulates various aspects of travel implementation. This paper highlights a demand for integrating various approaches ...
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Jiaxu Zhao, Binting Su, Xuli Rao and Zhide Chen
In this paper, we build a recommender system for a new study area: social commerce, which combines rich information about social network users and products on an e-commerce platform. The idea behind this recommender system is that a social network contai...
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Antoine Falconnet, Constantinos K. Coursaris, Joerg Beringer, Wietske Van Osch, Sylvain Sénécal and Pierre-Majorique Léger
Advice-giving systems such as decision support systems and recommender systems (RS) utilize algorithms to provide users with decision support by generating ?advice? ranging from tailored alerts for situational exception events to product recommendations ...
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Hyeon Jo, Jong-hyun Hong and Joon Yeon Choeh
In recent years, virtual online communities have experienced rapid growth. These communities enable individuals to share and manage images or websites by employing tags. A collaborative tagging system (CTS) facilitates the process by which internet users...
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Chin-Yi Chen and Jih-Jeng Huang
Traditional movie recommendation systems are increasingly falling short in the contemporary landscape of abundant information and evolving user behaviors. This study introduced the temporal knowledge graph recommender system (TKGRS), a ground-breaking al...
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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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Michail Salampasis, Alkiviadis Katsalis, Theodosios Siomos, Marina Delianidi, Dimitrios Tektonidis, Konstantinos Christantonis, Pantelis Kaplanoglou, Ifigeneia Karaveli, Chrysostomos Bourlis and Konstantinos Diamantaras
Research into session-based recommendation systems (SBSR) has attracted a lot of attention, but each study focuses on a specific class of methods. This work examines and evaluates a large range of methods, from simpler statistical co-occurrence methods t...
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Suleiman Ali Alsaif, Minyar Sassi Hidri, Imen Ferjani, Hassan Ahmed Eleraky and Adel Hidri
For more than ten years, online job boards have provided their services to both job seekers and employers who want to hire potential candidates. The provided services are generally based on traditional information retrieval techniques, which may not be a...
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