Author name cluster

Sunghun Kim

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

6 papers

1 author row

TIST Journal 2025 Journal Article

Advancing Session-Based Recommendations with Atten-Mixer+: Dynamic and Adaptive Multi-Level Intent Mining

Peiyan Zhang
Jiayan Guo
Chaozhuo Li
Liying Kang
Jaeboum Kim
Jie Xu
Xi Zhang
Yan Zhang

Session-Based Recommendation (SBR) systems, traditionally reliant on complex Graph Neural Networks (GNNs), often face challenges with marginal performance improvements despite increased model complexity. In this article, we dissect the classical GNN-based SBR models and empirically find that the sophisticated GNN propagations might be redundant, given the readout module plays a significant role in GNN-based models. Based on this observation, we introduce Atten-Mixer+, an advanced iteration of our previously developed Multi-Level Attention Mixture Network (Atten-Mixer). Atten-Mixer+ forgoes GNN propagation in favor of a dynamic and adaptive readout process, tailored to the unique characteristics of each session. Different from the vanilla version, Atten-Mixer+ features the Adaptive Intent Scaler (AIS) layer, which dynamically determines the depth of multi-level user intent analysis and a soft allocation approach for generating user intent queries across entire user interaction sequences. This innovative design allows Atten-Mixer+ to capture a nuanced and comprehensive understanding of user behaviors, overcoming the limitations of fixed-length analysis. Empirical evaluations on benchmark datasets highlight Atten-Mixer+’s superior efficiency and effectiveness, marking a significant step forward in the predictive accuracy of SBR systems.

Details DOI

YNICL Journal 2024 Journal Article

Multimodal analysis of disease onset in Alzheimer’s disease using Connectome, Molecular, and genetics data

Sewook Oh
Sunghun Kim
Jong-eun Lee
Bo-yong Park
Ji Hye Won
Hyunjin Park

Alzheimer's disease (AD) and its related age at onset (AAO) are highly heterogeneous, due to the inherent complexity of the disease. They are affected by multiple factors, such as neuroimaging and genetic predisposition. Multimodal integration of various data types is necessary; however, it has been nontrivial due to the high dimensionality of each modality. We aimed to identify multimodal biomarkers of AAO in AD using an extended version of sparse canonical correlation analysis, in which we integrated two imaging modalities, functional magnetic resonance imaging (fMRI) and positron emission tomography (PET), and genetic data in the form of single-nucleotide polymorphisms (SNPs) obtained from the Alzheimer's disease neuroimaging initiative database. These three modalities cover low-to-high-level complementary information and offer multiscale insights into the AAO. We identified multivariate markers of AAO in AD using fMRI, PET, and SNP. Furthermore, the markers identified were largely consistent with those reported in the existing literature. In particular, our serial mediation analysis suggests that genetic variants influence the AAO in AD by indirectly affecting brain connectivity by mediation of amyloid-beta protein accumulation, supporting a plausible path in existing research. Our approach provides comprehensive biomarkers related to AAO in AD and offers novel multimodal insights into AD.

Details DOI

AAAI Conference 2019 Conference Paper

Paraphrase Diversification Using Counterfactual Debiasing

Sunghyun Park
Seung-won Hwang
Fuxiang Chen
Jaegul Choo
Jung-Woo Ha
Sunghun Kim
Jinyeong Yim

The problem of generating a set of diverse paraphrase sentences while (1) not compromising the original meaning of the original sentence, and (2) imposing diversity in various semantic aspects, such as a lexical or syntactic structure, is examined. Existing work on paraphrase generation has focused more on the former, and the latter was trained as a fixed style transfer, such as transferring from positive to negative sentiments, even at the cost of losing semantics. In this work, we consider style transfer as a means of imposing diversity, with a paraphrasing correctness constraint that the target sentence must remain a paraphrase of the original sentence. However, our goal is to maximize the diversity for a set of k generated paraphrases, denoted as the diversified paraphrase (DP) problem. Our key contribution is deciding the style guidance at generation towards the direction of increasing the diversity of output with respect to those generated previously. As pre-materializing training data for all style decisions is impractical, we train with biased data, but with debiasing guidance. Compared to state-of-the-art methods, our proposed model can generate more diverse and yet semantically consistent paraphrase sentences. That is, our model, trained with the MSCOCO dataset, achieves the highest embedding scores, .94/. 95/. 86, similar to state-of-the-art results, but with a lower mBLEU score (more diverse) by 8. 73%.

PDF Details

IJCAI Conference 2017 Conference Paper

DeepAM: Migrate APIs with Multi-modal Sequence to Sequence Learning

Xiaodong Gu
Hongyu Zhang
Dongmei Zhang
Sunghun Kim

Computer programs written in one language are often required to be ported to other languages to support multiple devices and environments. When programs use language specific APIs (Application Programming Interfaces), it is very challenging to migrate these APIs to the corresponding APIs written in other languages. Existing approaches mine API mappings from projects that have corresponding versions in two languages. They rely on the sparse availability of bilingual projects, thus producing a limited number of API mappings. In this paper, we propose an intelligent system called DeepAM for automatically mining API mappings from a large-scale code corpus without bilingual projects. The key component of DeepAM is based on the multi-modal sequence to sequence learning architecture that aims to learn joint semantic representations of bilingual API sequences from big source code data. Experimental results indicate that DeepAM significantly increases the accuracy of API mappings as well as the number of API mappings when compared with the state-of-the-art approaches.

PDF Details

AAAI Conference 2011 Conference Paper

CosTriage: A Cost-Aware Triage Algorithm for Bug Reporting Systems

Jin-woo Park
Mu-Woong Lee
Jinhan Kim
Seung-won Hwang
Sunghun Kim

‘Who can ﬁx this bug? ’ is an important question in bug triage to “accurately” assign developers to bug reports. To address this question, recent research treats it as a optimizing recommendation accuracy problem and proposes a solution that is essentially an instance of content-based recommendation (CBR). However, CBR is well-known to cause over-specialization, recommending only the types of bugs that each developer has solved before. This problem is critical in practice, as some experienced developers could be overloaded, and this would slow the bug ﬁxing process. In this paper, we take two directions to address this problem: First, we reformulate the problem as an optimization problem of both accuracy and cost. Second, we adopt a content-boosted collaborative ﬁltering (CBCF), combining an existing CBR with a collaborative ﬁltering recommender (CF), which enhances the recommendation quality of either approach alone. However, unlike general recommendation scenarios, bug ﬁx history is extremely sparse. Due to the nature of bug ﬁxes, one bug is ﬁxed by only one developer, which makes it challenging to pursue the above two directions. To address this challenge, we develop a topic-model to reduce the sparseness and enhance the quality of CBCF. Our experimental evaluation shows that our solution reduces the cost efﬁciently by 30% without seriously compromising accuracy.

PDF Details

AAAI Conference 2010 Conference Paper

Towards an Intelligent Code Search Engine

Jinhan Kim
Sanghoon Lee
Seung-won Hwang
Sunghun Kim

Software developers increasingly rely on information from the Web, such as documents or code examples on Application Programming Interfaces (APIs), to facilitate their development processes. However, API documents often do not include enough information for developers to fully understand the API usages, while searching for good code examples requires non-trivial effort. To address this problem, we propose a novel code search engine, combining the strength of browsing documents and searching for code examples, by returning documents embedded with high-quality code example summaries mined from the Web. Our evaluation results show that our approach provides code examples with high precision and boosts programmer productivity.

PDF Details