Search Results for author: Hwanjo Yu

Found 44 papers, 23 papers with code

Top-Personalized-K Recommendation

no code implementations26 Feb 2024 Wonbin Kweon, SeongKu Kang, Sanghwan Jang, Hwanjo Yu

To address this issue, we introduce Top-Personalized-K Recommendation, a new recommendation task aimed at generating a personalized-sized ranking list to maximize individual user satisfaction.

Deep Rating Elicitation for New Users in Collaborative Filtering

1 code implementation26 Feb 2024 Wonbin Kweon, SeongKu Kang, Junyoung Hwang, Hwanjo Yu

Recent recommender systems started to use rating elicitation, which asks new users to rate a small seed itemset for inferring their preferences, to improve the quality of initial recommendations.

Distillation from Heterogeneous Models for Top-K Recommendation

1 code implementation2 Mar 2023 SeongKu Kang, Wonbin Kweon, Dongha Lee, Jianxun Lian, Xing Xie, Hwanjo Yu

Our work aims to transfer the ensemble knowledge of heterogeneous teachers to a lightweight student model using knowledge distillation (KD), to reduce the huge inference costs while retaining high accuracy.

Knowledge Distillation Recommendation Systems +1

Learning Topology-Specific Experts for Molecular Property Prediction

1 code implementation27 Feb 2023 Su Kim, Dongha Lee, SeongKu Kang, Seonghyeon Lee, Hwanjo Yu

In this paper, motivated by this observation, we propose TopExpert to leverage topology-specific prediction models (referred to as experts), each of which is responsible for each molecular group sharing similar topological semantics.

Molecular Property Prediction Property Prediction

Dynamic Multi-Behavior Sequence Modeling for Next Item Recommendation

no code implementations28 Jan 2023 Junsu Cho, Dongmin Hyun, Dong won Lim, Hyeon jae Cheon, Hyoung-iel Park, Hwanjo Yu

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.

Recommendation Systems

Topic Taxonomy Expansion via Hierarchy-Aware Topic Phrase Generation

no code implementations18 Oct 2022 Dongha Lee, Jiaming Shen, Seonghyeon Lee, Susik Yoon, Hwanjo Yu, Jiawei Han

Topic taxonomies display hierarchical topic structures of a text corpus and provide topical knowledge to enhance various NLP applications.

Relation Taxonomy Expansion

Beyond Learning from Next Item: Sequential Recommendation via Personalized Interest Sustainability

1 code implementation14 Sep 2022 Dongmin Hyun, Chanyoung Park, Junsu Cho, Hwanjo Yu

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.

Sequential Recommendation

Toward Interpretable Semantic Textual Similarity via Optimal Transport-based Contrastive Sentence Learning

1 code implementation ACL 2022 Seonghyeon Lee, Dongha Lee, Seongbo Jang, Hwanjo Yu

In the end, we propose CLRCMD, a contrastive learning framework that optimizes RCMD of sentence pairs, which enhances the quality of sentence similarity and their interpretation.

Contrastive Learning Language Modelling +5

Consensus Learning from Heterogeneous Objectives for One-Class Collaborative Filtering

1 code implementation26 Feb 2022 SeongKu Kang, Dongha Lee, Wonbin Kweon, Junyoung Hwang, Hwanjo Yu

ConCF constructs a multi-branch variant of a given target model by adding auxiliary heads, each of which is trained with heterogeneous objectives.

Collaborative Filtering

TaxoCom: Topic Taxonomy Completion with Hierarchical Discovery of Novel Topic Clusters

no code implementations18 Jan 2022 Dongha Lee, Jiaming Shen, SeongKu Kang, Susik Yoon, Jiawei Han, Hwanjo Yu

Topic taxonomies, which represent the latent topic (or category) structure of document collections, provide valuable knowledge of contents in many applications such as web search and information filtering.

Clustering Topic coverage

Obtaining Calibrated Probabilities with Personalized Ranking Models

1 code implementation9 Dec 2021 Wonbin Kweon, SeongKu Kang, Hwanjo Yu

Extensive evaluations with various personalized ranking models on real-world datasets show that both the proposed calibration methods and the unbiased empirical risk minimization significantly improve the calibration performance.

Image Classification

Out-of-Category Document Identification Using Target-Category Names as Weak Supervision

no code implementations24 Nov 2021 Dongha Lee, Dongmin Hyun, Jiawei Han, Hwanjo Yu

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.

Learnable Structural Semantic Readout for Graph Classification

no code implementations22 Nov 2021 Dongha Lee, Su Kim, Seonghyeon Lee, Chanyoung Park, Hwanjo Yu

By the help of a global readout operation that simply aggregates all node (or node-cluster) representations, existing GNN classifiers obtain a graph-level representation of an input graph and predict its class label using the representation.

Graph Classification Position

Weakly Supervised Temporal Anomaly Segmentation with Dynamic Time Warping

1 code implementation ICCV 2021 Dongha Lee, Sehun Yu, Hyunjun Ju, Hwanjo Yu

Most recent studies on detecting and localizing temporal anomalies have mainly employed deep neural networks to learn the normal patterns of temporal data in an unsupervised manner.

Dynamic Time Warping Segmentation

OoMMix: Out-of-manifold Regularization in Contextual Embedding Space for Text Classification

no code implementations ACL 2021 Seonghyeon Lee, Dongha Lee, Hwanjo Yu

Recent studies on neural networks with pre-trained weights (i. e., BERT) have mainly focused on a low-dimensional subspace, where the embedding vectors computed from input words (or their contexts) are located.

Data Augmentation text-classification +1

Unsupervised Proxy Selection for Session-based Recommender Systems

1 code implementation8 Jul 2021 Junsu Cho, SeongKu Kang, Dongmin Hyun, Hwanjo Yu

Session-based Recommender Systems (SRSs) have been actively developed to recommend the next item of an anonymous short item sequence (i. e., session).

Recommendation Systems

Topology Distillation for Recommender System

no code implementations16 Jun 2021 SeongKu Kang, Junyoung Hwang, Wonbin Kweon, Hwanjo Yu

To address this issue, we propose a novel method named Hierarchical Topology Distillation (HTD) which distills the topology hierarchically to cope with the large capacity gap.

Knowledge Distillation Model Compression +1

Bidirectional Distillation for Top-K Recommender System

1 code implementation5 Jun 2021 Wonbin Kweon, SeongKu Kang, Hwanjo Yu

Recommender systems (RS) have started to employ knowledge distillation, which is a model compression technique training a compact model (student) with the knowledge transferred from a cumbersome model (teacher).

Knowledge Distillation Model Compression +1

Out-of-Manifold Regularization in Contextual Embedding Space for Text Classification

1 code implementation14 May 2021 Seonghyeon Lee, Dongha Lee, Hwanjo Yu

Recent studies on neural networks with pre-trained weights (i. e., BERT) have mainly focused on a low-dimensional subspace, where the embedding vectors computed from input words (or their contexts) are located.

Data Augmentation text-classification +1

Bootstrapping User and Item Representations for One-Class Collaborative Filtering

no code implementations13 May 2021 Dongha Lee, SeongKu Kang, Hyunjun Ju, Chanyoung Park, Hwanjo Yu

To make the representations of positively-related users and items similar to each other while avoiding a collapsed solution, BUIR adopts two distinct encoder networks that learn from each other; the first encoder is trained to predict the output of the second encoder as its target, while the second encoder provides the consistent targets by slowly approximating the first encoder.

Collaborative Filtering Data Augmentation

Learning Heterogeneous Temporal Patterns of User Preference for Timely Recommendation

1 code implementation29 Apr 2021 Junsu Cho, Dongmin Hyun, SeongKu Kang, Hwanjo Yu

Existing studies regard the time information as a single type of feature and focus on how to associate it with user preferences on items.

Recommendation Systems

Learnable Dynamic Temporal Pooling for Time Series Classification

no code implementations2 Apr 2021 Dongha Lee, Seonghyeon Lee, Hwanjo Yu

With the increase of available time series data, predicting their class labels has been one of the most important challenges in a wide range of disciplines.

Classification Dynamic Time Warping +4

Multi-Class Data Description for Out-of-distribution Detection

1 code implementation2 Apr 2021 Dongha Lee, Sehun Yu, Hwanjo Yu

The capability of reliably detecting out-of-distribution samples is one of the key factors in deploying a good classifier, as the test distribution always does not match with the training distribution in most real-world applications.

Out-of-Distribution Detection Test

One-class Classification Robust to Geometric Transformation

no code implementations1 Jan 2021 Hyunjun Ju, Dongha Lee, SeongKu Kang, Hwanjo Yu

Recent studies on one-class classification have achieved a remarkable performance, by employing the self-supervised classifier that predicts the geometric transformation applied to in-class images.

Classification General Classification +2

DE-RRD: A Knowledge Distillation Framework for Recommender System

2 code implementations8 Dec 2020 SeongKu Kang, Junyoung Hwang, Wonbin Kweon, Hwanjo Yu

Recent recommender systems have started to employ knowledge distillation, which is a model compression technique distilling knowledge from a cumbersome model (teacher) to a compact model (student), to reduce inference latency while maintaining performance.

Knowledge Distillation Model Compression +1

Building Large-Scale English and Korean Datasets for Aspect-Level Sentiment Analysis in Automotive Domain

1 code implementation COLING 2020 Dongmin Hyun, Junsu Cho, Hwanjo Yu

We release large-scale datasets of users{'} comments in two languages, English and Korean, for aspect-level sentiment analysis in automotive domain.

Sentiment Analysis

Interest Sustainability-Aware Recommender System

1 code implementation Conference 2020 Dongmin Hyun, Junsu Cho, Chanyoung Park, Hwanjo Yu

More precisely, we first predict the interest sustainability of each item, that is, how likely each item will be consumed in the future.

Collaborative Filtering Recommendation Systems

Sparse Network Inversion for Key Instance Detection in Multiple Instance Learning

no code implementations7 Sep 2020 Beomjo Shin, Junsu Cho, Hwanjo Yu, Seungjin Choi

Since a positive bag contains both positive and negative instances, it is often required to detect positive instances (key instances) when a set of instances is categorized as a positive bag.

Multiple Instance Learning

Unsupervised Differentiable Multi-aspect Network Embedding

1 code implementation7 Jun 2020 Chanyoung Park, Carl Yang, Qi Zhu, Donghyun Kim, Hwanjo Yu, Jiawei Han

To capture the multiple aspects of each node, existing studies mainly rely on offline graph clustering performed prior to the actual embedding, which results in the cluster membership of each node (i. e., node aspect distribution) fixed throughout training of the embedding model.

Clustering Graph Clustering +2

BHIN2vec: Balancing the Type of Relation in Heterogeneous Information Network

no code implementations26 Nov 2019 Seonghyeon Lee, Chanyoung Park, Hwanjo Yu

We view the heterogeneous network embedding as simultaneously solving multiple tasks in which each task corresponds to each relation type in a network.

Network Embedding Node Classification +2

Unsupervised Attributed Multiplex Network Embedding

2 code implementations15 Nov 2019 Chanyoung Park, Donghyun Kim, Jiawei Han, Hwanjo Yu

Even for those that consider the multiplexity of a network, they overlook node attributes, resort to node labels for training, and fail to model the global properties of a graph.

Network Embedding Relation

Deep Generative Classifier for Out-of-distribution Sample Detection

no code implementations25 Sep 2019 Dongha Lee, Sehun Yu, Hwanjo Yu

The capability of reliably detecting out-of-distribution samples is one of the key factors in deploying a good classifier, as the test distribution always does not match with the training distribution in most real-world applications.

Test

Step Size Optimization

no code implementations25 Sep 2019 Gyoung S. Na, Dongmin Hyeon, Hwanjo Yu

This paper proposes a new approach for step size adaptation in gradient methods.

Out-of-Distribution Image Detection Using the Normalized Compression Distance

no code implementations25 Sep 2019 Sehun Yu, Donga Lee, Hwanjo Yu

Inspired by the method using the global average pooling on the feature maps of the convolutional neural networks, the goal of our method is to extract informative sequential patterns from the feature maps.

Out-of-Distribution Detection

Collaborative Translational Metric Learning

1 code implementation4 Jun 2019 Chanyoung Park, Donghyun Kim, Xing Xie, Hwanjo Yu

We also conduct extensive qualitative evaluations on the translation vectors learned by our proposed method to ascertain the benefit of adopting the translation mechanism for implicit feedback-based recommendations.

Knowledge Graph Embedding Metric Learning +1

Task-Guided Pair Embedding in Heterogeneous Network

1 code implementation4 Jun 2019 Chanyoung Park, Donghyun Kim, Qi Zhu, Jiawei Han, Hwanjo Yu

In this paper, we propose a novel task-guided pair embedding framework in heterogeneous network, called TaPEm, that directly models the relationship between a pair of nodes that are related to a specific task (e. g., paper-author relationship in author identification).

Network Embedding

Click-aware purchase prediction with push at the top

no code implementations21 Jun 2017 Chanyoung Park, Donghyun Kim, Min-Chul Yang, Jung-Tae Lee, Hwanjo Yu

We begin by formulating various model assumptions, each one assuming a different order of user preferences among purchased, clicked-but-not-purchased, and non-clicked items, to study the usefulness of leveraging click records.

Learning-To-Rank

Federated Tensor Factorization for Computational Phenotyping

no code implementations11 Apr 2017 Yejin Kim, Jimeng Sun, Hwanjo Yu, Xiaoqian Jiang

In this paper, we developed a novel solution to enable federated tensor factorization for computational phenotyping without sharing patient-level data.

Computational Phenotyping

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