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Found 16 papers, 14 papers with code
Density of States Prediction of Crystalline Materials via Prompt-guided Multi-Modal Transformer
While previous works mainly focus on obtaining high-quality representations of crystalline materials for DOS prediction, we focus on predicting the DOS from the obtained representations by reflecting the nature of DOS: DOS determines the general distribution of states as a function of energy.
MUSE: Music Recommender System with Shuffle Play Recommendation Enhancement
Recommender systems have become indispensable in music streaming services, enhancing user experiences by personalizing playlists and facilitating the serendipitous discovery of new music.
Conditional Graph Information Bottleneck for Molecular Relational Learning
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.
MELT: Mutual Enhancement of Long-Tailed User and Item for Sequential Recommendation
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.
Predicting Density of States via Multi-modal Transformer
The density of states (DOS) is a spectral property of materials, which provides fundamental insights on various characteristics of materials.
Dynamic Multi-Behavior Sequence Modeling for Next Item Recommendation
To this end, we first address the characteristics of multi-behavior sequences that should be considered in SRSs, and then propose novel methods for Dynamic Multi-behavior Sequence modeling named DyMuS, which is a light version, and DyMuS+, which is an improved version, considering the characteristics.
Generating Multiple-Length Summaries via Reinforcement Learning for Unsupervised Sentence Summarization
We formulate the unsupervised summarization based on the Markov decision process with rewards representing the summary quality.
Heterogeneous Graph Learning for Multi-modal Medical Data Analysis
In this paper, we propose an effective graph-based framework called HetMed (Heterogeneous Graph Learning for Multi-modal Medical Data Analysis) for fusing the multi-modal medical data.
Beyond Learning from Next Item: Sequential Recommendation via Personalized Interest Sustainability
We first formulate a task that requires to predict which items each user will consume in the recent period of the training time based on users' consumption history.
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Relational Self-Supervised Learning on Graphs
Despite their success, existing GRL methods tend to overlook an inherent distinction between images and graphs, i. e., images are assumed to be independently and identically distributed, whereas graphs exhibit relational information among data instances, i. e., nodes.
GraFN: Semi-Supervised Node Classification on Graph with Few Labels via Non-Parametric Distribution Assignment
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 solely based on the supervision obtained from the labeled nodes.
Out-of-Category Document Identification Using Target-Category Names as Weak Supervision
To address this challenge, we introduce a new task referred to as out-of-category detection, which aims to distinguish the documents according to their semantic relevance to the inlier (or target) categories by using the category names as weak supervision.
Unsupervised Proxy Selection for Session-based Recommender Systems
Session-based Recommender Systems (SRSs) have been actively developed to recommend the next item of an anonymous short item sequence (i. e., session).
Learning Heterogeneous Temporal Patterns of User Preference for Timely Recommendation
Existing studies regard the time information as a single type of feature and focus on how to associate it with user preferences on items.
Building Large-Scale English and Korean Datasets for Aspect-Level Sentiment Analysis in Automotive Domain
We release large-scale datasets of users{'} comments in two languages, English and Korean, for aspect-level sentiment analysis in automotive domain.
Interest Sustainability-Aware Recommender System
More precisely, we first predict the interest sustainability of each item, that is, how likely each item will be consumed in the future.
