Zobrazeno 1 - 10
of 35
pro vyhledávání: '"Hu, Hengchang"'
Autor:
Tourni, Isidora Chara, Guo, Lei, Hu, Hengchang, Halim, Edward, Ishwar, Prakash, Daryanto, Taufiq, Jalal, Mona, Chen, Boqi, Betke, Margrit, Zhafransyah, Fabian, Lai, Sha, Wijaya, Derry Tanti
News media structure their reporting of events or issues using certain perspectives. When describing an incident involving gun violence, for example, some journalists may focus on mental health or gun regulation, while others may emphasize the discus
Externí odkaz:
http://arxiv.org/abs/2406.17213
Autor:
Liu, Qijiong, Dong, Xiaoyu, Xiao, Jiaren, Chen, Nuo, Hu, Hengchang, Zhu, Jieming, Zhu, Chenxu, Sakai, Tetsuya, Wu, Xiao-Ming
Vector quantization, renowned for its unparalleled feature compression capabilities, has been a prominent topic in signal processing and machine learning research for several decades and remains widely utilized today. With the emergence of large mode
Externí odkaz:
http://arxiv.org/abs/2405.03110
This paper aims to efficiently enable large language models (LLMs) to use external knowledge and goal guidance in conversational recommender system (CRS) tasks. Advanced LLMs (e.g., ChatGPT) are limited in domain-specific CRS tasks for 1) generating
Externí odkaz:
http://arxiv.org/abs/2405.01868
Incorporating item content information into click-through rate (CTR) prediction models remains a challenge, especially with the time and space constraints of industrial scenarios. The content-encoding paradigm, which integrates user and item encoders
Externí odkaz:
http://arxiv.org/abs/2403.08206
Autor:
Wu, Jiahao, Liu, Qijiong, Hu, Hengchang, Fan, Wenqi, Liu, Shengcai, Li, Qing, Wu, Xiao-Ming, Tang, Ke
Modern techniques in Content-based Recommendation (CBR) leverage item content information to provide personalized services to users, but suffer from resource-intensive training on large datasets. To address this issue, we explore the dataset condensa
Externí odkaz:
http://arxiv.org/abs/2310.09874
Fairness is a widely discussed topic in recommender systems, but its practical implementation faces challenges in defining sensitive features while maintaining recommendation accuracy. We propose feature fairness as the foundation to achieve equitabl
Externí odkaz:
http://arxiv.org/abs/2309.15418
Conversational recommender systems (CRS) generate recommendations through an interactive process. However, not all CRS approaches use human conversations as their source of interaction data; the majority of prior CRS work simulates interactions by ex
Externí odkaz:
http://arxiv.org/abs/2309.07682
In sequential recommendation, multi-modal information (e.g., text or image) can provide a more comprehensive view of an item's profile. The optimal stage (early or late) to fuse modality features into item representations is still debated. We propose
Externí odkaz:
http://arxiv.org/abs/2308.15980
Autor:
Liu, Xu, Liang, Yuxuan, Huang, Chao, Hu, Hengchang, Cao, Yushi, Hooi, Bryan, Zimmermann, Roger
Spatio-temporal graph neural networks (STGNN) have become the most popular solution to traffic forecasting. While successful, they rely on the message passing scheme of GNNs to establish spatial dependencies between nodes, and thus inevitably inherit
Externí odkaz:
http://arxiv.org/abs/2301.12603
Prerequisites can play a crucial role in users' decision-making yet recommendation systems have not fully utilized such contextual background knowledge. Traditional recommendation systems (RS) mostly enrich user-item interactions where the context co
Externí odkaz:
http://arxiv.org/abs/2209.11471