Meta-Learning with Adaptive Weighted Loss for Imbalanced Cold-Start Recommendation

Sequential recommenders have made great strides in capturing a user's preferences. Nevertheless, the cold-start recommendation remains a fundamental challenge as they typically involve limited user-item interactions for personalization. Recently, gradient-based meta-learning approaches have emerged in the sequential recommendation field due to their fast adaptation and easy-to-integrate abilities. The meta-learning algorithms formulate the cold-start recommendation as a few-shot learning problem, where each user is represented as a task to be adapted. While meta-learning algorithms generally assume that task-wise samples are evenly distributed over classes or values, user-item interactions in real-world applications do not conform to such a distribution (e.g., watching favorite videos multiple times, leaving only positive ratings without any negative ones). Consequently, imbalanced user feedback, which accounts for the majority of task training data, may dominate the user adaptation process and prevent meta-learning algorithms from learning meaningful meta-knowledge for personalized recommendations. To alleviate this limitation, we propose a novel sequential recommendation framework based on gradient-based meta-learning that captures the imbalanced rating distribution of each user and computes adaptive loss for user-specific learning. Our work is the first to tackle the impact of imbalanced ratings in cold-start sequential recommendation scenarios. Through extensive experiments conducted on real-world datasets, we demonstrate the effectiveness of our framework.Comment: Accepted by CIKM 202

PU GNN: Chargeback Fraud Detection in P2E MMORPGs via Graph Attention Networks with Imbalanced PU Labels

The recent advent of play-to-earn (P2E) systems in massively multiplayer online role-playing games (MMORPGs) has made in-game goods interchangeable with real-world values more than ever before. The goods in the P2E MMORPGs can be directly exchanged with cryptocurrencies such as Bitcoin, Ethereum, or Klaytn via blockchain networks. Unlike traditional in-game goods, once they had been written to the blockchains, P2E goods cannot be restored by the game operation teams even with chargeback fraud such as payment fraud, cancellation, or refund. To tackle the problem, we propose a novel chargeback fraud prediction method, PU GNN, which leverages graph attention networks with PU loss to capture both the players' in-game behavior with P2E token transaction patterns. With the adoption of modified GraphSMOTE, the proposed model handles the imbalanced distribution of labels in chargeback fraud datasets. The conducted experiments on three real-world P2E MMORPG datasets demonstrate that PU GNN achieves superior performances over previously suggested methods.Comment: Under Review, Industry Trac

3DTeethSeg'22: 3D Teeth Scan Segmentation and Labeling Challenge

Teeth localization, segmentation, and labeling from intra-oral 3D scans are essential tasks in modern dentistry to enhance dental diagnostics, treatment planning, and population-based studies on oral health. However, developing automated algorithms for teeth analysis presents significant challenges due to variations in dental anatomy, imaging protocols, and limited availability of publicly accessible data. To address these challenges, the 3DTeethSeg'22 challenge was organized in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2022, with a call for algorithms tackling teeth localization, segmentation, and labeling from intraoral 3D scans. A dataset comprising a total of 1800 scans from 900 patients was prepared, and each tooth was individually annotated by a human-machine hybrid algorithm. A total of 6 algorithms were evaluated on this dataset. In this study, we present the evaluation results of the 3DTeethSeg'22 challenge. The 3DTeethSeg'22 challenge code can be accessed at: https://github.com/abenhamadou/3DTeethSeg22_challengeComment: 29 pages, MICCAI 2022 Singapore, Satellite Event, Challeng

Who Needs What Aspects of L2 English to What Levels of Proficiency?

This paper explores who needs what aspects of L2 English to what levels of proficiency, focusing on college English curriculum development in Korea. A survey was conducted of 532 college students in Seoul. Grounded that career is one of the most important motives for EFL learning, the participants were divided into seven groups based on their desired careers. The results show that the types of desired careers of the participants are highly correlative with the strength and weakness of their need for learning English, the kinds of English skills they want to learn, and the levels of English proficiency they hope to achieve

Acute Hepatitis A-Induced Autoimmune Hepatitis: A Case Report and Literature Review

Introduction: The pathogenesis of autoimmune hepatitis (AIH) is little known. Previous case reports suggest that several viral hepatitis, including hepatitis A, can trigger AIH. Patient: A 55-year-old female showed general weakness and jaundice. The patient was diagnosed with acute hepatitis A and discharged after 14 days of hospitalization with improving liver function. However, blood tests performed 6 days after discharge revealed an increase in liver enzymes and high serum titers of an anti-nuclear antibody and immunoglobulin G. She was readmitted for liver biopsy. Diagnosis: Liver biopsy showed acute hepatitis A along with AIH. According to the revised international autoimmune hepatitis group scoring system, her score was 14 and she was diagnosed as AIH induced by acute hepatitis A. Intervention: Conservative treatments with crystalloid (Lactated Ringer’s Solution), ursodeoxycholic acid, and silymarin were administered. Outcomes: The patient has been followed up on an outpatient basis and neither symptom recurrence nor an increase in liver enzymes has been reported thus far. Lessons: After the treatment of acute hepatitis A, liver function needs to be carefully monitored over time, and the possibility of autoimmune hepatitis should be considered when liver enzymes increases