Preprints

Continual Low-Rank Adapters for LLM-based Generative Recommender Systems [link]

Hyunsik Yoo, Ting-Wei Li, SeongKu Kang, Zhining Liu, Charlie Xu, Qilin Qi, Hanghang Tong

• We propose PESO (Proximally rEgularized Single evolving lOra, a continual adaptation method for LoRA in recommendation

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

Wonbin Kweon, Runchu Tian, SeongKu Kang, Pengcheng Jiang, Zhiyong Lu, Jiawei Han, Hwanjo Yu

• We propose PairSem that represents relevant semantics as entity-aspect pairs, capturing complex, multi-faceted scientific concepts.

LLM-Based Compact Reranking with Document Features for Scientific Retrieval [link]

Runchu Tian, Xueqiang Xu, Bowen Jin, SeongKu Kang, Jiawei Han

• We propose CORANK, a training-free, model-agnostic reranking framework for scientific retrieval.

Graph Signal Processing for Cross-Domain Recommendation [link]

Jeongeun Lee, SeongKu Kang, Won-Yong Shin, Jeongwhan Choi, Noseong Park, Dongha Lee

• We propose CGSP, a unified cross-domain recommendation framework based on graph signal processing.

Why These Documents? Explainable Generative Retrieval with Hierarchical Category Paths [link]

Sangam Lee, Ryang Heo, SeongKu Kang, Susik Yoon, Jinyoung Yeo, Dongha Lee

• We propose HyPE, which leverages hierarchical category paths as explanation, progressing from broad to specific semantic categories. 

SC-Rec: Enhancing Generative Retrieval with Self-Consistent Reranking for Sequential Recommendation [link]

Tongyoung Kim, Soojin Yoon, SeongKu Kang, Jinyoung Yeo, Dongha Lee

• We propose SCREC, a unified recommender system that learns diverse preference knowledge from two distinct item indices and multiple prompt templates.