Zobrazeno 1 - 10
of 742
pro vyhledávání: '"Wang, Jianling"'
Sequential recommender systems aims to predict the users' next interaction through user behavior modeling with various operators like RNNs and attentions. However, existing models generally fail to achieve the three golden principles for sequential r
Externí odkaz:
http://arxiv.org/abs/2406.12580
Autor:
Wang, Jianling, Lu, Haokai, Liu, Yifan, Ma, He, Wang, Yueqi, Gu, Yang, Zhang, Shuzhou, Han, Ningren, Bi, Shuchao, Baugher, Lexi, Chi, Ed, Chen, Minmin
Traditional recommendation systems are subject to a strong feedback loop by learning from and reinforcing past user-item interactions, which in turn limits the discovery of novel user interests. To address this, we introduce a hybrid hierarchical fra
Externí odkaz:
http://arxiv.org/abs/2405.16363
With the capabilities of understanding and executing natural language instructions, Large language models (LLMs) can potentially act as a powerful tool for textual data augmentation. However, the quality of augmented data depends heavily on the augme
Externí odkaz:
http://arxiv.org/abs/2404.17642
Publikováno v:
In European Conference on Information Retrieval 2024, vol 14612 (pp. 75-89)
Collaborative filtering (CF) based recommendations suffer from mainstream bias -- where mainstream users are favored over niche users, leading to poor recommendation quality for many long-tail users. In this paper, we identify two root causes of this
Externí odkaz:
http://arxiv.org/abs/2404.08887
Sequential recommendation aims to estimate the dynamic user preferences and sequential dependencies among historical user behaviors. Although Transformer-based models have proven to be effective for sequential recommendation, they suffer from the inf
Externí odkaz:
http://arxiv.org/abs/2403.03900
The reasoning and generalization capabilities of LLMs can help us better understand user preferences and item characteristics, offering exciting prospects to enhance recommendation systems. Though effective while user-item interactions are abundant,
Externí odkaz:
http://arxiv.org/abs/2402.11724
Autor:
Wang, Jianling, Hammer, Francois, Yang, Yanbin, Pawlowski, Marcel S., Mamon, Gary A., Wang, Haifeng
Most Milky Way dwarf galaxies are much less bound to their host than are relics of Gaia-Sausage-Enceladus and Sgr. These dwarfs are expected to have fallen into the Galactic halo less than 3 Gyr ago, and will therefore have undergone no more than one
Externí odkaz:
http://arxiv.org/abs/2311.05687
Autor:
Hammer, Francois, Wang, Jianling, Mamon, Gary A., Pawlowski, Marcel S., Yang, Yanbin, Jiao, Yongjun, Li, Hefan, Bonifacio, Piercarlo, Caffau, Elisabetta, Wang, Haifeng
We study how structural properties of globular clusters and dwarf galaxies are linked to their orbits in the Milky Way halo. From the inner to the outer halo, orbital energy increases and stellar-systems gradually move out of internal equilibrium: in
Externí odkaz:
http://arxiv.org/abs/2311.05677
Autor:
Jiao, Yongjun, Hammer, Francois, Wang, Haifeng, Wang, Jianling, Amram, Philippe, Chemin, Laurent, Yang, Yanbin
Publikováno v:
A&A 678, A208 (2023)
Our position inside the Galactic disc had prevented us from establishing an accurate rotation curve, until the advent of Gaia, whose third data release (Gaia DR3) made it possible to specify it up to twice the optical radius. We aim to establish a ne
Externí odkaz:
http://arxiv.org/abs/2309.00048
As powerful tools for representation learning on graphs, graph neural networks (GNNs) have played an important role in applications including social networks, recommendation systems, and online web services. However, GNNs have been shown to be vulner
Externí odkaz:
http://arxiv.org/abs/2308.15614