Zobrazeno 1 - 10
of 11
pro vyhledávání: '"Dongmin Hyun"'
Publikováno v:
Bioinformatics.
Motivation Single-cell RNA sequencing enables researchers to study cellular heterogeneity at single-cell level. To this end, identifying cell types of cells with clustering techniques becomes an important task for downstream analysis. However, challe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3851ee2a5ecbfe1418fc7a1489604f52
https://doi.org/10.1093/bioinformatics/btad342
https://doi.org/10.1093/bioinformatics/btad342
Publikováno v:
Information Sciences. 545:595-607
Review-based recommender systems represent users and items with reviews associated with them. As such, the recommender systems are highly dependent on the number of reviews, which is usually few in number. Thus, they produce inaccurate recommendation
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2833c4c074ae29ed22c777776f63a92f
https://doi.org/10.1016/j.ins.2020.09.025
https://doi.org/10.1016/j.ins.2020.09.025
Sequential recommender systems have shown effective suggestions by capturing users' interest drift. There have been two groups of existing sequential models: user- and item-centric models. The user-centric models capture personalized interest drift b
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bf929bb65fdfcda5dd0520b527aa97a1
Despite the success of Graph Neural Networks (GNNs) on various applications, GNNs encounter significant performance degradation when the amount of supervision signals, i.e., number of labeled nodes, is limited, which is expected as GNNs are trained s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6b2c3aa9375c605f79c0900bd11591d8
Publikováno v:
2021 IEEE International Conference on Data Mining (ICDM).
Identifying outlier documents, whose content is different from the majority of the documents in a corpus, has played an important role to manage a large text collection. However, due to the absence of explicit information about the inlier (or target)
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::af51cb6f911400d3b764db698945933a
https://doi.org/10.1109/icdm51629.2021.00041
https://doi.org/10.1109/icdm51629.2021.00041
Publikováno v:
Information Sciences. 491:166-178
Target-level sentiment analysis (TLSA) is a classification task to extract sentiments from targets in text. In this paper, we propose t arget-dependent c onvolutional n eural n etwork ( TCNN ) tailored to the task of TLSA. The TCNN leverages the dist
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4eb043b140fabbc9ae336b4b6854a32c
https://doi.org/10.1016/j.ins.2019.03.076
https://doi.org/10.1016/j.ins.2019.03.076
Publikováno v:
Proceedings of the Web Conference 2021.
Recommender systems have achieved great success in modeling user's preferences on items and predicting the next item the user would consume. Recently, there have been many efforts to utilize time information of users' interactions with items to captu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5f6adc5925f54b01c60e46d05ba7ef93
https://doi.org/10.1145/3442381.3449947
https://doi.org/10.1145/3442381.3449947
Session-based Recommender Systems (SRSs) have been actively developed to recommend the next item of an anonymous short item sequence (i.e., session). Unlike sequence-aware recommender systems where the whole interaction sequence of each user can be u
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3dcc2a482a68fa0bde03aa220e559b0c
Publikováno v:
ICDM
The key to successful recommendations is to provide users with items likely to be consumed in the future. From realworld data, we observe that users' consumption patterns for items change over time. For example, users may no longer like some items th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4ebfc1f8eafea440d6c68491d7c20343
https://doi.org/10.1109/icdm50108.2020.00028
https://doi.org/10.1109/icdm50108.2020.00028
Publikováno v:
COLING
We release large-scale datasets of users’ comments in two languages, English and Korean, for aspect-level sentiment analysis in automotive domain. The datasets consist of 58,000+ commentaspect pairs, which are the largest compared to existing datas
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6004279e9a3d7efd9346357eae835ffd
https://doi.org/10.18653/v1/2020.coling-main.83
https://doi.org/10.18653/v1/2020.coling-main.83