Gyuseok Lee

Intelligent Data Science and Applications Lab. Our mission is to develop intelligent systems that empower individuals to navigate this overwhelming sea of information effectively. We aim to deliver relevant, high-quality, and actionable insights tailored to each user's unique context and needs.

Continual Low-Rank Adapters for LLM-based Generative Recommender Systems

Continual Recommender Systems: A Focus on LLMs and Evolving Trends

Capturing User Interests from Data Streams for Continual Sequential Recommendation

PairSem: LLM-Guided Pairwise Semantic Matching for Scientific Document Retrieval

CREAM: Continual Retrieval on Dynamic Streaming Corpora with Adaptive Soft Memory

BPL: Bias-adaptive Preference Distillation Learning for Recommender System

Topic Coverage-based Demonstration Retrieval for In-Context Learning

Scientific Paper Retrieval with LLM-Guided Semantic-Based Ranking

Continual Recommender Systems

DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning

Imagine All The Relevance: Scenario-Profiled Indexing with Knowledge Expansion for Dense Retrieval

Delving into Instance-Dependent Label Noise in Graph Data: A Comprehensive Study and Benchmark

Embracing Plasticity: Balancing Stability and Plasticity in Continual Recommender Systems

Personalized Preference Reasoning with Large Language Models for Accurate and Explainable Recommendation

Uncertainty Quantification and Decomposition for LLM-based Recommendation

Chain-of-Factors Paper-Reviewer Matching

Improving Scientific Document Retrieval with Concept Coverage-based Query Set Generation

Unsupervised Robust Cross-Lingual Entity Alignment via Neighbor Triple Matching with Entity and Relation Texts

Taxonomy-guided Semantic Indexing for Academic Paper Search

Continual Collaborative Distillation for Recommender System

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

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

Multi-Domain Sequential Recommendation via Domain Space Learning

Learning Discriminative Dynamics with Label Corruption for Noisy Label Detection

Unbiased, Effective, and Efficient Distillation from Heterogeneous Models for Recommender Systems

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

Top-Personalized-K Recommendation

Multi-Domain Recommendation to Attract Users via Domain Preference Modeling

MvFS: Multi-view Feature Selection for Recommender System

Distillation from Heterogeneous Models for Top-K Recommendation

Learning Topology-Specific Experts for Molecular Property Prediction

Consensus Learning from Heterogeneous Objectives for One-Class Collaborative Filtering

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

Obtaining Calibrated Probabilities with Personalized Ranking Models

Mitigating viewpoint sensitivity of self-supervised one-class classifiers

Personalized Knowledge Distillation for Recommender System

Topology Distillation for Recommender System

Bootstrapping User and Item Representations for One-Class Collaborative Filtering

Unsupervised Proxy Selection for Session-based Recommender Systems

Learning Heterogeneous Temporal Patterns of User Preference for Timely Recommendation

Bidirectional Distillation for Top-K Recommender System

Item-side Ranking Regularized Distillation for Recommender System

DE-RRD: A Knowledge Distillation Framework for Recommender System

Deep Rating Elicitation for New Users in Collaborative Filtering

Multi-Modal Component Embedding for Fake News Detection

Semi-Supervised Learning for Cross-Domain Recommendation to Cold-start Users

Densifying a Trust Network for Effective Collaborative Filtering

Semi-supervised learning for cross-domain recommendation to cold-start users

Bidirectional distillation for top-K recommender system

Deepretrieval: Hacking real search engines and retrievers with large language models via reinforcement learning

Taxocom: Topic taxonomy completion with hierarchical discovery of novel topic clusters

Learning heterogeneous temporal patterns of user preference for timely recommendation

Learning topology-specific experts for molecular property prediction

Distillation from heterogeneous models for top-k recommendation

Personalized knowledge distillation for recommender system

Review-driven Personalized Preference Reasoning with Large Language Models for Recommendation

Unsupervised proxy selection for session-based recommender systems

Pearl: A review-driven persona-knowledge grounded conversational recommendation dataset

Obtaining calibrated probabilities with personalized ranking models

Improving retrieval in theme-specific applications using a corpus topical taxonomy

Mvfs: Multi-view feature selection for recommender system

Item-side ranking regularized distillation for recommender system

Top-personalized-k recommendation

Self-consistent reasoning-based aspect-sentiment quad prediction with extract-then-assign strategy

Hwanjo Yu

Dongha Lee

Wonbin Kweon

Jiawei Han

Jinyoung Yeo

Sanghwan Jang

Bowen Jin

Susik Yoon

Runchu Tian

Hyunsik Yoo

Junsu Cho

Dongmin Hyun

Chanyoung Park

Jianxun Lian

Xing Xie 谢幸

Suyeon Kim

Jiaming Shen

Seonghyeon Lee

Shivam Agarwal

SeongKu Kang

Providing accurate recommendations to newly joined users (or potential users, so-called cold-start users) has remained a challenging yet important problem in recommender systems. To infer the preferences of such cold-start users based on their preferences observed in other domains, several cross-domain recommendation (CDR) methods have been studied. The state-of-the-art Embedding and Mapping approach for CDR (EMCDR) aims to infer the latent vectors of cold-start users by supervised mapping from the latent space of another domain. In this paper, we propose a novel CDR framework based on semi-supervised mapping, called SSCDR, which effectively learns the cross-domain relationship even in the case that only a few number of labeled data is available. To this end, it first learns the latent vectors of users and items for each domain so that their interactions are represented by the distances, then …

The goal of one-class collaborative filtering (OCCF) is to identify the user-item pairs that are positively-related but have not been interacted yet, where only a small portion of positive user-item interactions (e.g., users' implicit feedback) are observed. For discriminative modeling between positive and negative interactions, most previous work relied on negative sampling to some extent, which refers to considering unobserved user-item pairs as negative, as actual negative ones are unknown. However, the negative sampling scheme has critical limitations because it may choose "positive but unobserved" pairs as negative. This paper proposes a novel OCCF framework, named as BUIR, which does not require negative sampling. 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 …

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. The state-of-the-art methods have only focused on making the student model to accurately imitate the predictions of the teacher model. They have a limitation in that the prediction results incompletely reveal the teacher's knowledge. In this paper, we propose a novel knowledge distillation framework for recommender system, called DE-RRD, which enables the student model to learn from the latent knowledge encoded in the teacher model as well as from the teacher's predictions. Concretely, DE-RRD consists of two methods: 1) Distillation Experts (DE) that directly transfers the latent knowledge from the teacher model. DE exploits "experts" and a novel …

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). The state-of-the-art methods rely on unidirectional distillation transferring the knowledge only from the teacher to the student, with an underlying assumption that the teacher is always superior to the student. However, we demonstrate that the student performs better than the teacher on a significant proportion of the test set, especially for RS. Based on this observation, we propose Bidirectional Distillation (BD) framework whereby both the teacher and the student collaboratively improve with each other. Specifically, each model is trained with the distillation loss that makes to follow the other’s prediction along with its original loss function. For effective bidirectional distillation, we propose rank …

Information retrieval systems are crucial for enabling effective access to large document collections. Recent approaches have leveraged Large Language Models (LLMs) to enhance retrieval performance through query augmentation, but often rely on expensive supervised learning or distillation techniques that require significant computational resources and hand-labeled data. We introduce DeepRetrieval, a reinforcement learning (RL) approach that trains LLMs for query generation through trial and error without supervised data (reference query). Using retrieval metrics as rewards, our system generates queries that maximize retrieval performance. DeepRetrieval outperforms leading methods on literature search with 65.07% (vs. previous SOTA 24.68%) recall for publication search and 63.18% (vs. previous SOTA 32.11%) recall for trial search using real-world search engines. DeepRetrieval also dominates in evidence-seeking retrieval, classic information retrieval and SQL database search. With only 3B parameters, it outperforms industry-leading models like GPT-4o and Claude-3.5-Sonnet on 11/13 datasets. These results demonstrate that our RL approach offers a more efficient and effective paradigm for information retrieval. Our data and code are available at: https://github.com/pat-jj/DeepRetrieval.

Recommender Systems (RS) have employed knowledge distillation which is a model compression technique training a compact student model with the knowledge transferred from a pre-trained large teacher model. Recent work has shown that transferring knowledge from the teacher's intermediate layer significantly improves the recommendation quality of the student. However, they transfer the knowledge of individual representation point-wise and thus have a limitation in that primary information of RS lies in the relations in the representation space. This paper proposes a new topology distillation approach that guides the student by transferring the topological structure built upon the relations in the teacher space. We first observe that simply making the student learn the whole topological structure is not always effective and even degrades the student's performance. We demonstrate that because the capacity of …

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. Recently, several unsupervised methods have been developed to automatically construct the topic taxonomy from a text corpus, but it is challenging to generate the desired taxonomy without any prior knowledge. In this paper, we study how to leverage the partial (or incomplete) information about the topic structure as guidance to find out the complete topic taxonomy. We propose a novel framework for topic taxonomy completion, named TaxoCom, which recursively expands the topic taxonomy by discovering novel sub-topic clusters of terms and documents. To effectively identify novel topics within a hierarchical topic structure, TaxoCom devises its embedding and clustering techniques to be closely-linked with …

Recommender systems have achieved great success in modeling user’s preferences on items and predicting the next item the user would consume. Recently, there have been many efforts to utilize time information of users’ interactions with items to capture inherent temporal patterns of user behaviors and offer timely recommendations at a given time. Existing studies regard the time information as a single type of feature and focus on how to associate it with user preferences on items. However, we argue they are insufficient for fully learning the time information because the temporal patterns of user preference are usually heterogeneous. A user’s preference for a particular item may 1) increase periodically or 2) evolve over time under the influence of significant recent events, and each of these two kinds of temporal pattern appears with some unique characteristics. In this paper, we first define the unique …

Recently, graph neural networks (GNNs) have been successfully applied to predicting molecular properties, which is one of the most classical cheminformatics tasks with various applications. Despite their effectiveness, we empirically observe that training a single GNN model for diverse molecules with distinct structural patterns limits its prediction performance. 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. That is, each expert learns topology-specific discriminative features while being trained with its corresponding topological group. To tackle the key challenge of grouping molecules by their topological patterns, we introduce a clustering-based gating module that assigns an input molecule into one of the clusters and further optimizes the gating module with two different types of self-supervision: topological semantics induced by GNNs and molecular scaffolds, respectively. Extensive experiments demonstrate that TopExpert has boosted the performance for molecular property prediction and also achieved better generalization for new molecules with unseen scaffolds than baselines. The code is available at https://github. com/kimsu55/ToxExpert.

Recent recommender systems have shown remarkable performance by using an ensemble of heterogeneous models. However, it is exceedingly costly because it requires resources and inference latency proportional to the number of models, which remains the bottleneck for production. 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. Through an empirical study, we find that the efficacy of distillation severely drops when transferring knowledge from heterogeneous teachers. Nevertheless, we show that an important signal to ease the difficulty can be obtained from the teacher’s training trajectory. This paper proposes a new KD framework, named HetComp, that guides the student model by transferring easy-to-hard sequences of knowledge generated from …

Nowadays, Knowledge Distillation (KD) has been widely studied for recommender system. KD is a model-independent strategy that generates a small but powerful student model by transferring knowledge from a pre-trained large teacher model. Recent work has shown that the knowledge from the teacher’s representation space significantly improves the student model. The state-of-the-art method, named Distillation Experts (DE), adopts cluster-wise distillation that transfers the knowledge of each representation cluster separately to distill the various preference knowledge in a balanced manner. However, it is challenging to apply DE to a new environment since its performance is highly dependent on several key assumptions and hyperparameters that need to be tuned for each dataset and each base model. In this work, we propose a novel method, dubbed Personalized Hint Regression (PHR), distilling the …

Session-based Recommender Systems (SRSs) have been actively developed to recommend the next item of an anonymous short item sequence (i.e., session). Unlike sequence-aware recommender systems where the whole interaction sequence of each user can be used to model both the short-term interest and the general interest of the user, the absence of user-dependent information in SRSs makes it difficult to directly derive the user's general interest from data. Therefore, existing SRSs have focused on how to effectively model the information about short-term interest within the sessions, but they are insufficient to capture the general interest of users. To this end, we propose a novel framework to overcome the limitation of SRSs, named ProxySR, which imitates the missing information in SRSs (i.e., general interest of users) by modeling proxies of sessions. ProxySR selects a proxy for the input session in an …

Conversational recommender systems are an emerging area that has garnered increasing interest in the community, especially with the advancements in large language models (LLMs) that enable sophisticated handling of conversational input. Despite the progress, the field still has many aspects left to explore. The currently available public datasets for conversational recommendation lack specific user preferences and explanations for recommendations, hindering high-quality recommendations. To address such challenges, we present a novel conversational recommendation dataset named PEARL, synthesized with persona-and knowledge-augmented LLM simulators. We obtain detailed persona and knowledge from real-world reviews and construct a large-scale dataset with over 57k dialogues. Our experimental results demonstrate that PEARL contains more specific user preferences, show expertise in the target domain, and provides recommendations more relevant to the dialogue context than those in prior datasets. Furthermore, we demonstrate the utility of PEARL by showing that our downstream models outperform baselines in both human and automatic evaluations. We release our dataset and code.

For personalized ranking models, the well-calibrated probability of an item being preferred by a user has great practical value. While existing work shows promising results in image classification, probability calibration has not been much explored for personalized ranking. In this paper, we aim to estimate the calibrated probability of how likely a user will prefer an item. We investigate various parametric distributions and propose two parametric calibration methods, namely Gaussian calibration and Gamma calibration. Each proposed method can be seen as a post-processing function that maps the ranking scores of pre-trained models to well-calibrated preference probabilities, without affecting the recommendation performance. We also design the unbiased empirical risk minimization framework that guides the calibration methods to learning of true preference probability from the biased user-item interaction dataset. 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.

Document retrieval has greatly benefited from the advancements of large-scale pre-trained language models (PLMs). However, their effectiveness is often limited in theme-specific applications for specialized areas or industries, due to unique terminologies, incomplete contexts of user queries, and specialized search intents. To capture the theme-specific information and improve retrieval, we propose to use a corpus topical taxonomy, which outlines the latent topic structure of the corpus while reflecting user-interested aspects. We introduce ToTER (Topical Taxonomy Enhanced Retrieval) framework, which identifies the central topics of queries and documents with the guidance of the taxonomy, and exploits their topical relatedness to supplement missing contexts. As a plug-and-play framework, ToTER can be flexibly employed to enhance various PLM-based retrievers. Through extensive quantitative, ablative, and …

Feature selection, which is a technique to select key features in recommender systems, has received increasing research attention. Recently, Adaptive Feature Selection (AdaFS) has shown remarkable performance by adaptively selecting features for each data instance, considering that the importance of a given feature field can vary significantly across data. However, this method still has limitations in that its selection process could be easily biased to major features that frequently occur. To address these problems, we propose Multi-view Feature Selection (MvFS), which selects informative features for each instance more effectively. Most importantly, MvFS employs a multi-view network consisting of multiple sub-networks, each of which learns to measure the feature importance of a part of data with different feature patterns. By doing so, MvFS mitigates the bias problem towards dominant patterns and promotes a …

Recent recommender system (RS) have adopted large and sophisticated model architecture to better understand the complex user-item relationships, and accordingly, the size of the recommender is continuously increasing. To reduce the high inference costs of the large recommender, knowledge distillation (KD), which is a model compression technique from a large pre-trained model (teacher) to a small model (student), has been actively studied for RS. The state-of-the-art method is based on the ranking distillation approach, which makes the student preserve the ranking orders among items predicted by the teacher. In this work, we propose a new regularization method designed to maximize the effect of the ranking distillation in RS. We first point out an important limitation and a room for improvement of the state-of-the-art ranking distillation method based on our in-depth analysis.Then, we introduce the item-side …

The conventional top-K recommendation, which presents the top-K items with the highest ranking scores, is a common practice for generating personalized ranking lists. However, is this fixed-size top-K recommendation the optimal approach for every user's satisfaction? Not necessarily. We point out that providing fixed-size recommendations without taking into account user utility can be suboptimal, as it may unavoidably include irrelevant items or limit the exposure to relevant ones. 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. As a solution to the proposed task, we develop a model-agnostic framework named PerK. PerK estimates the expected user utility by leveraging calibrated interaction probabilities, subsequently selecting the recommendation size that …

In the task of aspect sentiment quad prediction (ASQP), generative methods for predicting sentiment quads have shown promisingresults. However, they still suffer from imprecise predictions and limited interpretability, caused by data scarcity and inadequate modeling of the quadruplet composition process. In this paper, we propose Self-Consistent Reasoning-based Aspect sentiment quadruple Prediction (SCRAP), optimizing its model to generate reasonings and the corresponding sentiment quadruplets in sequence. SCRAP adopts the Extract-Then-Assign reasoning strategy, which closely mimics human cognition. In the end, SCRAP significantly improves the model’s ability to handle complex reasoning tasks and correctly predict quadruplets through consistency voting, resulting in enhanced interpretability and accuracy in ASQP.

IDEA Lab

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연구실 논문 목록

Continual Low-Rank Adapters for LLM-based Generative Recommender Systems

Continual Recommender Systems: A Focus on LLMs and Evolving Trends

Capturing User Interests from Data Streams for Continual Sequential Recommendation

연구실 보유 특허

대학별 입시끈

우리 연구실 모집

교수님 소개

강성구

연구실 키워드

알럼나이가 간 회사

연구실 지원 방법

대우 조건

연도별 논문 인용수

저널 & 학회 논문 출판 수

연구실 논문 목록

Continual Low-Rank Adapters for LLM-based Generative Recommender Systems

Continual Recommender Systems: A Focus on LLMs and Evolving Trends

Capturing User Interests from Data Streams for Continual Sequential Recommendation

연구실 보유 특허

최근 3년간 연구실적 높은 연구실

대학별 입시끈

우리 연구실 모집