arXiv subjects

Kibum Kim

Publications and source records attributed to Kibum Kim.

22 records · Page 2Linked to original sources

arXiv2023

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. While many existing studies address the long-tailed problem in SRS, they only focus on either the user or item perspective. However, we discover that the long-tailed user and item problems exist at the same time, and considering only either one of them leads to sub-optimal performance of the other one. In this paper, we propose a novel framework for SRS, called Mutual Enhancement of Long-Tailed user and item (MELT), that jointly alleviates the long-tailed problem in the perspectives of both users and items. MELT consists of bilateral branches each of which is responsible for long-tailed users and items, respectively, and the branches are trained to mutually enhance each other, which is trained effectively by a curriculum learning-based training. MELT is model-agnostic in that it can be seamlessly integrated with existing SRS models. Extensive experiments on eight datasets demonstrate the benefit of alleviating the long-tailed problems in terms of both users and items even without sacrificing the performance of head users and items, which has not been achieved by existing methods. To the best of our knowledge, MELT is the first work that jointly alleviates the long-tailed user and item problems in SRS.

cs.IR↗

arXiv2022

LTE4G: Long-Tail Experts for Graph Neural Networks

Existing Graph Neural Networks (GNNs) usually assume a balanced situation where both the class distribution and the node degree distribution are balanced. However, in real-world situations, we often encounter cases where a few classes (i.e., head class) dominate other classes (i.e., tail class) as well as in the node degree perspective, and thus naively applying existing GNNs eventually fall short of generalizing to the tail cases. Although recent studies proposed methods to handle long-tail situations on graphs, they only focus on either the class long-tailedness or the degree long-tailedness. In this paper, we propose a novel framework for training GNNs, called Long-Tail Experts for Graphs (LTE4G), which jointly considers the class long-tailedness, and the degree long-tailedness for node classification. The core idea is to assign an expert GNN model to each subset of nodes that are split in a balanced manner considering both the class and degree long-tailedness. After having trained an expert for each balanced subset, we adopt knowledge distillation to obtain two class-wise students, i.e., Head class student and Tail class student, each of which is responsible for classifying nodes in the head classes and tail classes, respectively. We demonstrate that LTE4G outperforms a wide range of state-of-the-art methods in node classification evaluated on both manual and natural imbalanced graphs. The source code of LTE4G can be found at https://github.com/SukwonYun/LTE4G.

cs.LG↗

arXiv2021

Remote Virtual Showdown: A Collaborative Virtual Reality Game for People with Visual Impairments

Many researchers have developed VR systems for people with visual impairments by using various audio feedback techniques. However, there has been much less study of collaborative VR systems in which people with visual impairments and people with able-body can participate together. Therefore, we developed a VR showdown game which is similar to a real Showdown game in which two players can play together in the same virtual environment. We incorporate auditory distance perception using the HRTF (Head Related Transform Function) based on a spatial position in VR. We developed two modes in the showdown game. One is the PVA (Player vs. Agent) mode in which people with visual impairments can play alone and the PVP (Player vs. Player) mode in which people with visual impairments can play with another player in the network environment. We conducted our user studies by comparing the performances of people with visual impairments and people with able-body. The user study results show that people with visual impairments won 67.6% of the games when competing against people with able-body. This paper reports an example of a collaborative VR system for people with visual impairments and also design guideline for developing VR systems for people with visual impairments.

cs.HC↗

arXiv2001

Optical phonons of superconducting infinite-layer compounds Sr_{0.9}Ln_{0.1}CuO_2 (Ln = La, Gd, Sm)

We have identifies the otical phonon modes of the superconducting infinite-layer compounds Sr$_{0.9}Ln_{0.1}$CuO$_2$ ($Ln$ = La, Gd, Sm; $T_c$ = 43 K for all) from their infrared reflectivity spectra obtained with a Fourier-transform infrared spectrometer on high-quality high-purity samples synthesized under high pressure. The La- and the Gd-doped compounds exhibited only four (2$A_{\rm 2u}$+2$E_{\rm u}$) out of the five (2$A_{\rm 2u}$+3$E_{\rm u}$) infrared-active phonon modes predicted by a group theoretical analysis whereas the Sm-doped compound exhibited all five modes. We propose the atomic displacement pattern for each phonon mode based on reported lattice dynamics calculations and through comparison with the phonon modes of other single-layer high-$T_{\rm c}$ cuprate superconductors.

cond-mat.supr-con↗