Výsledky vyhledávání

Report

Pacer and Runner: Cooperative Learning Framework between Single- and Cross-Domain Sequential Recommendation

Autor: Park, Chung, Kim, Taesan, Yoon, Hyungjun, Hong, Junui, Yu, Yelim, Cho, Mincheol, Choi, Minsung, Choo, Jaegul

Cross-Domain Sequential Recommendation (CDSR) improves recommendation performance by utilizing information from multiple domains, which contrasts with Single-Domain Sequential Recommendation (SDSR) that relies on a historical interaction within a spe

Externí odkaz: http://arxiv.org/abs/2407.11245

Zobrazit plný text záznamu

Report

FedGeo: Privacy-Preserving User Next Location Prediction with Federated Learning

Autor: Park, Chung, Choi, Taekyoon, Kim, Taesan, Cho, Mincheol, Hong, Junui, Choi, Minsung, Choo, Jaegul

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 applications. Previous studies using large-scale trajectory datasets in a si

Externí odkaz: http://arxiv.org/abs/2312.04594

Zobrazit plný text záznamu

Report

Cracking the Code of Negative Transfer: A Cooperative Game Theoretic Approach for Cross-Domain Sequential Recommendation

Autor: Park, Chung, Kim, Taesan, Choi, Taekyoon, Hong, Junui, Yu, Yelim, Cho, Mincheol, Lee, Kyunam, Ryu, Sungil, Yoon, Hyungjun, Choi, Minsung, Choo, Jaegul

This paper investigates Cross-Domain Sequential Recommendation (CDSR), a promising method that uses information from multiple domains (more than three) to generate accurate and diverse recommendations, and takes into account the sequential nature of

Externí odkaz: http://arxiv.org/abs/2311.13188

Zobrazit plný text záznamu

Report

PASTA: PArallel Spatio-Temporal Attention with spatial auto-correlation gating for fine-grained crowd flow prediction

Autor: Park, Chung, Hong, Junui, Park, Cheonbok, Kim, Taesan, Choi, Minsung, Choo, Jaegul

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 a method for predicting future city-wide crowd flows by modeling the

Externí odkaz: http://arxiv.org/abs/2310.02284

Zobrazit plný text záznamu

Report

Pre-training Contextual Location Embeddings in Personal Trajectories via Efficient Hierarchical Location Representations

Autor: Park, Chung, Kim, Taesan, Hong, Junui, Choi, Minsung, Choo, Jaegul

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 too expensive, due to the large number of locations to be trained in

Externí odkaz: http://arxiv.org/abs/2310.01252

Zobrazit plný text záznamu

Akademický článek

Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.

Vyhledávací nástroje:

Upřesnit hledání