Search Results for author: SeongKu Kang

Found 19 papers, 11 papers with code

Multi-Domain Recommendation to Attract Users via Domain Preference Modeling

no code implementations26 Mar 2024 Hyuunjun Ju, SeongKu Kang, Dongha Lee, Junyoung Hwang, Sanghwan Jang, Hwanjo Yu

Targeting a platform that operates multiple service domains, we introduce a new task, Multi-Domain Recommendation to Attract Users (MDRAU), which recommends items from multiple ``unseen'' domains with which each user has not interacted yet, by using knowledge from the user's ``seen'' domains.

Improving Retrieval in Theme-specific Applications using a Corpus Topical Taxonomy

1 code implementation7 Mar 2024 SeongKu Kang, Shivam Agarwal, Bowen Jin, Dongha Lee, Hwanjo Yu, Jiawei Han

Document retrieval has greatly benefited from the advancements of large-scale pre-trained language models (PLMs).

Retrieval

Pearl: A Review-driven Persona-Knowledge Grounded Conversational Recommendation Dataset

no code implementations7 Mar 2024 Minjin Kim, Minju Kim, Hana Kim, Beong-woo Kwak, Soyeon Chun, Hyunseo Kim, SeongKu Kang, Youngjae Yu, Jinyoung Yeo, Dongha Lee

Our experimental results demonstrate that utterances in PEARL include more specific user preferences, show expertise in the target domain, and provide recommendations more relevant to the dialogue context than those in prior datasets.

Recommendation Systems

Self-Consistent Reasoning-based Aspect-Sentiment Quad Prediction with Extract-Then-Assign Strategy

no code implementations1 Mar 2024 Jieyong Kim, Ryang Heo, Yongsik Seo, SeongKu Kang, Jinyoung Yeo, Dongha Lee

In the task of aspect sentiment quad prediction (ASQP), generative methods for predicting sentiment quads have shown promising results.

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.

Collaborative Filtering Recommendation Systems

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.

MvFS: Multi-view Feature Selection for Recommender System

1 code implementation5 Sep 2023 Youngjune Lee, Yeongjong Jeong, Keunchan Park, SeongKu Kang

Feature selection, which is a technique to select key features in recommender systems, has received increasing research attention.

Feature Importance feature selection +1

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

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

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

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

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

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