8 papers
A Dual-Expert Strategy Integrating LLMs to Mitigate Negative Transfer in Cross-Domain Sequential Recommendation
Hyeongjun Yun, Kihyuk Song, Jaegul Choo +1
Cross-Domain Sequential Recommendation (CDSR) predicts the next item a user will interact with based on their historical interaction sequences across multiple domains. Recent appro…
Don't Let Bandit Feedback Pull Continual LLM-Recommender Updates Off Target
Taesan Kim, Hyeongjun Yun, Jaegul Choo +1
Generative LLM-based recommenders (LLM-Rec) require continual post-deployment updates, yet deployment logs provide only policy-shaped contextual bandit feedback: outcomes are obser…
Towards Trustworthy LLM-Based Recommendation via Rationale Integration
Chung Park, Taesan Kim, Hyeongjun Yun +7
Traditional recommender systems (RS) have been primarily optimized for accuracy and short-term engagement, often overlooking transparency and trustworthiness. Recently, platforms s…
Pacer and Runner: Cooperative Learning Framework between Single- and Cross-Domain Sequential Recommendation
Chung Park, Taesan Kim, Hyungjun Yoon +5
Cross-Domain Sequential Recommendation (CDSR) improves recommendation performance by utilizing information from multiple domains, which contrasts with Single-Domain Sequential Reco…
FedGeo: Privacy-Preserving User Next Location Prediction with Federated Learning
Chung Park, Taekyoon Choi, Taesan Kim +4
A User Next Location Prediction (UNLP) task, which predicts the next location that a user will move to given his/her trajectory, is an indispensable task for a wide range of applic…
Cracking the Code of Negative Transfer: A Cooperative Game Theoretic Approach for Cross-Domain Sequential Recommendation
Chung Park, Taesan Kim, Taekyoon Choi +8
This paper investigates Cross-Domain Sequential Recommendation (CDSR), a promising method that uses information from multiple domains (more than three) to generate accurate and div…
PASTA: PArallel Spatio-Temporal Attention with spatial auto-correlation gating for fine-grained crowd flow prediction
Chung Park, Junui Hong, Cheonbok Park +3
Understanding the movement patterns of objects (e.g., humans and vehicles) in a city is essential for many applications, including city planning and management. This paper proposes…
Pre-training Contextual Location Embeddings in Personal Trajectories via Efficient Hierarchical Location Representations
Chung Park, Taesan Kim, Junui Hong +2
Pre-training the embedding of a location generated from human mobility data has become a popular method for location based services. In practice, modeling the location embedding is…