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Hybrid methods such as collaborative deep learning (CDL) and collaborative variational autoencoder (CVAE) have become state-of-the-art methods in recommender systems for scienti_c articles. However, t...hey typically use only information from titles and abstracts of arti-cles, and ignore potentially useful information in the tags and citations. Therefore, they may miss articles that contain vastly di_erent content from other articles, although those articles present the same topic. We addressed this problem by developing the CiT-CVAE model that consid- ers tag and citation information when providing recommendations. Our experimental results indicate that the proposed model achieves consis- tent improvement compared with CDL and CVAE.続きを見る
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