Zobrazeno 1 - 10
of 33
pro vyhledávání: '"Hyun, Dongmin"'
The density of states (DOS) is a spectral property of crystalline materials, which provides fundamental insights into various characteristics of the materials. While previous works mainly focus on obtaining high-quality representations of crystalline
Externí odkaz:
http://arxiv.org/abs/2311.12856
Recommender systems have become indispensable in music streaming services, enhancing user experiences by personalizing playlists and facilitating the serendipitous discovery of new music. However, the existing recommender systems overlook the unique
Externí odkaz:
http://arxiv.org/abs/2308.09649
Molecular relational learning, whose goal is to learn the interaction behavior between molecular pairs, got a surge of interest in molecular sciences due to its wide range of applications. Recently, graph neural networks have recently shown great suc
Externí odkaz:
http://arxiv.org/abs/2305.01520
Publikováno v:
SIGIR (2023), 68-77
The long-tailed problem is a long-standing challenge in Sequential Recommender Systems (SRS) in which the problem exists in terms of both users and items. While many existing studies address the long-tailed problem in SRS, they only focus on either t
Externí odkaz:
http://arxiv.org/abs/2304.08382
The density of states (DOS) is a spectral property of materials, which provides fundamental insights on various characteristics of materials. In this paper, we propose a model to predict the DOS by reflecting the nature of DOS: DOS determines the gen
Externí odkaz:
http://arxiv.org/abs/2303.07000
Sequential Recommender Systems (SRSs) aim to predict the next item that users will consume, by modeling the user interests within their item sequences. While most existing SRSs focus on a single type of user behavior, only a few pay attention to mult
Externí odkaz:
http://arxiv.org/abs/2301.12105
Sentence summarization shortens given texts while maintaining core contents of the texts. Unsupervised approaches have been studied to summarize texts without human-written summaries. However, recent unsupervised models are extractive, which remove w
Externí odkaz:
http://arxiv.org/abs/2212.10843
Routine clinical visits of a patient produce not only image data, but also non-image data containing clinical information regarding the patient, i.e., medical data is multi-modal in nature. Such heterogeneous modalities offer different and complement
Externí odkaz:
http://arxiv.org/abs/2211.15158
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:
http://arxiv.org/abs/2209.06644
Over the past few years, graph representation learning (GRL) has been a powerful strategy for analyzing graph-structured data. Recently, GRL methods have shown promising results by adopting self-supervised learning methods developed for learning repr
Externí odkaz:
http://arxiv.org/abs/2208.10493