14 citations · 21 across the 12 of their papers we have counts for
12 papers
On the Memorization Behavior of LLMs in Generative Recommendation: Observations, Implications, and Training Strategies
Sunwoo Kim, Sunkyung Lee, Clark Mingxuan Ju +5
Generative recommendation (GR) has emerged as a promising direction for recommender systems. Recently, large language models (LLMs) have been increasingly adopted for GR, as their…
From Relevance to Authority: Authority-aware Generative Retrieval in Web Search Engines
Sunkyung Lee, Jihye Back, Donghyeon Jeon +4
Generative information retrieval (GenIR) formulates the retrieval process as a text-to-text generation task, leveraging the vast knowledge of large language models. However, existi…
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…
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…
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…
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…
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…
ConQueR: Contextualized Query Reduction using Search Logs
Hye-young Kim, Minjin Choi, Sunkyung Lee +3
Query reformulation is a key mechanism to alleviate the linguistic chasm of query in ad-hoc retrieval. Among various solutions, query reduction effectively removes extraneous terms…
SpaDE: Improving Sparse Representations using a Dual Document Encoder for First-stage Retrieval
Eunseong Choi, Sunkyung Lee, Minjin Choi +3
Sparse document representations have been widely used to retrieve relevant documents via exact lexical matching. Owing to the pre-computed inverted index, it supports fast ad-hoc s…
MelBERT: Metaphor Detection via Contextualized Late Interaction using Metaphorical Identification Theories
Minjin Choi, Sunkyung Lee, Eunseong Choi +4
Automated metaphor detection is a challenging task to identify metaphorical expressions of words in a sentence. To tackle this problem, we adopt pre-trained contextualized models,…