31 citations · 40 across the 17 of their papers we have counts for
18 papers
ACE: Anisotropy-Controllable Embedding for LLM-enhanced Sequential Recommendation
Dongcheol Lee, Hye-young Kim, Jongwuk Lee
Recent advances in the LLM-as-Extractor paradigm leverage large language models (LLMs) to transfer semantically rich item embeddings into sequential recommendation (SR) backbones.…
HELIOS: Harmonizing Early Fusion, Late Fusion, and LLM Reasoning for Multi-Granular Table-Text Retrieval
Sungho Park, Joohyung Yun, Jongwuk Lee +1
Table-text retrieval aims to retrieve relevant tables and text to support open-domain question answering. Existing studies use either early or late fusion, but face limitations. Ea…
MergeRec: Model Merging for Data-Isolated Cross-Domain Sequential Recommendation
Hyunsoo Kim, Jaewan Moon, Seongmin Park +1
Modern recommender systems trained on domain-specific data often struggle to generalize across multiple domains. Cross-domain sequential recommendation has emerged as a promising r…
Enhancing Time Awareness in Generative Recommendation
Sunkyung Lee, Seongmin Park, Jonghyo Kim +2
Generative recommendation has emerged as a promising paradigm that formulates the recommendations into a text-to-text generation task, harnessing the vast knowledge of large langua…
LLM-Enhanced Linear Autoencoders for Recommendation
Jaewan Moon, Seongmin Park, Jongwuk Lee
Large language models (LLMs) have been widely adopted to enrich the semantic representation of textual item information in recommender systems. However, existing linear autoencoder…
MUFFIN: Mixture of User-Adaptive Frequency Filtering for Sequential Recommendation
Ilwoong Baek, Mincheol Yoon, Seongmin Park +1
Sequential recommendation (SR) aims to predict users' subsequent interactions by modeling their sequential behaviors. Recent studies have explored frequency domain analysis, which…
GRAM: Generative Recommendation via Semantic-aware Multi-granular Late Fusion
Sunkyung Lee, Minjin Choi, Eunseong Choi +2
Generative recommendation is an emerging paradigm that leverages the extensive knowledge of large language models by formulating recommendations into a text-to-text generation task…
DIFF: Dual Side-Information Filtering and Fusion for Sequential Recommendation
Hye-young Kim, Minjin Choi, Sunkyung Lee +2
Side-information Integrated Sequential Recommendation (SISR) benefits from auxiliary item information to infer hidden user preferences, which is particularly effective for sparse i…
Why is Normalization Necessary for Linear Recommenders?
Seongmin Park, Mincheol Yoon, Hye-young Kim +1
Despite their simplicity, linear autoencoder (LAE)-based models have shown comparable or even better performance with faster inference speed than neural recommender models. However…
Linear Item-Item Model with Neural Knowledge for Session-based Recommendation
Minjin Choi, Sunkyung Lee, Seongmin Park +1
Session-based recommendation (SBR) aims to predict users' subsequent actions by modeling short-term interactions within sessions. Existing neural models primarily focus on capturin…
Empowering Retrieval-based Conversational Recommendation with Contrasting User Preferences
Heejin Kook, Junyoung Kim, Seongmin Park +1
Conversational recommender systems (CRSs) are designed to suggest the target item that the user is likely to prefer through multi-turn conversations. Recent studies stress that cap…
Temporal Linear Item-Item Model for Sequential Recommendation
Seongmin Park, Mincheol Yoon, Minjin Choi +1
In sequential recommendation (SR), neural models have been actively explored due to their remarkable performance, but they suffer from inefficiency inherent to their complexity. On…
From Reading to Compressing: Exploring the Multi-document Reader for Prompt Compression
Eunseong Choi, Sunkyung Lee, Minjin Choi +2
Large language models (LLMs) have achieved significant performance gains using advanced prompting techniques over various tasks. However, the increasing length of prompts leads to…
MARS: Matching Attribute-aware Representations for Text-based Sequential Recommendation
Hyunsoo Kim, Junyoung Kim, Minjin Choi +2
Sequential recommendation aims to predict the next item a user is likely to prefer based on their sequential interaction history. Recently, text-based sequential recommendation has…
Multi-intent-aware Session-based Recommendation
Minjin Choi, Hye-young Kim, Hyunsouk Cho +1
Session-based recommendation (SBR) aims to predict the following item a user will interact with during an ongoing session. Most existing SBR models focus on designing sophisticated…
Multi-Granularity Guided Fusion-in-Decoder
Eunseong Choi, Hyeri Lee, Jongwuk Lee
In Open-domain Question Answering (ODQA), it is essential to discern relevant contexts as evidence and avoid spurious ones among retrieved results. The model architecture that uses…
GLEN: Generative Retrieval via Lexical Index Learning
Sunkyung Lee, Minjin Choi, Jongwuk Lee
Generative retrieval shed light on a new paradigm of document retrieval, aiming to directly generate the identifier of a relevant document for a query. While it takes advantage of…
Time-aware Random Walk Diffusion to Improve Dynamic Graph Learning
Jong-whi Lee, Jinhong Jung
How can we augment a dynamic graph for improving the performance of dynamic graph neural networks? Graph augmentation has been widely utilized to boost the learning performance of…