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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7707027 | PMC |
| http://dx.doi.org/10.3399/bjgp20X713789 | DOI Listing |
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A multi-agent reinforcement learning framework for cross-domain sequential recommendation.
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Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China), Hefei University of Technology, Hefei, 230009, Anhui, China. Electronic address:
Sequential recommendation models aim to predict the next item based on the sequence of items users interact with, ordered chronologically. However, these models face the challenge of data sparsity. Recent studies have explored cross-domain sequential recommendation, where users' interaction data across multiple source domains are leveraged to enhance recommendations in data-sparse target domains.
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Faculty of Dentistry, PHENIKAA University, Hanoi, Vietnam.
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From preparation to success: assessing the effectiveness of Saudi Pharmacists Licensure Examination (SPLE) preparation strategies in Saudi Arabia.
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View Article and Find Full Text PDFEffects of an ARCS-based interactive e-book featuring clinical scenarios on the learning motivation, self-efficacy and practical competence of nursing students in foetal heart rate interpretation.
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Department of Nursing, Far Eastern Memorial Hospital, New Taipei City 220303, Taiwan; Department of Nursing, Hsin Sheng Junior College of Medical Care and Management, Taoyuan 32544, Taiwan. Electronic address:
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