Zobrazeno 1 - 10
of 28
pro vyhledávání: '"Lyu, Fuyuan"'
Recently, the introduction of knowledge graphs (KGs) has significantly advanced recommender systems by facilitating the discovery of potential associations between items. However, existing methods still face several limitations. First, most KGs suffe
Externí odkaz:
http://arxiv.org/abs/2410.12229
Autor:
Zhang, Qiyuan, Wang, Yufei, YU, Tiezheng, Jiang, Yuxin, Wu, Chuhan, Li, Liangyou, Wang, Yasheng, Jiang, Xin, Shang, Lifeng, Tang, Ruiming, Lyu, Fuyuan, Ma, Chen
With significant efforts in recent studies, LLM-as-a-Judge has become a cost-effective alternative to human evaluation for assessing the text generation quality in a wide range of tasks. However, there still remains a reliability gap between LLM-as-a
Externí odkaz:
http://arxiv.org/abs/2410.05193
Customer Lifetime Value (CLTV) prediction is a critical task in business applications. Accurately predicting CLTV is challenging in real-world business scenarios, as the distribution of CLTV is complex and mutable. Firstly, there is a large number of
Externí odkaz:
http://arxiv.org/abs/2408.08585
Comprehensively understanding and accurately predicting the performance of large language models across diverse downstream tasks has emerged as a pivotal challenge in NLP research. The pioneering scaling law on downstream works demonstrated intrinsic
Externí odkaz:
http://arxiv.org/abs/2407.01300
Autor:
Qin, Shaoxiang, Lyu, Fuyuan, Peng, Wenhui, Geng, Dingyang, Wang, Ju, Tang, Xing, Leroyer, Sylvie, Gao, Naiping, Liu, Xue, Wang, Liangzhu Leon
In solving partial differential equations (PDEs), Fourier Neural Operators (FNOs) have exhibited notable effectiveness. However, FNO is observed to be ineffective with large Fourier kernels that parameterize more frequencies. Current solutions rely o
Externí odkaz:
http://arxiv.org/abs/2404.07200
As user behaviors become complicated on business platforms, online recommendations focus more on how to touch the core conversions, which are highly related to the interests of platforms. These core conversions are usually continuous targets, such as
Externí odkaz:
http://arxiv.org/abs/2403.17442
This study unveils the In-Context Evolutionary Search (ICE-SEARCH) method, which is among the first works that melds large language models (LLMs) with evolutionary algorithms for feature selection (FS) tasks and demonstrates its effectiveness in Medi
Externí odkaz:
http://arxiv.org/abs/2402.18609
Negative sampling methods are vital in implicit recommendation models as they allow us to obtain negative instances from massive unlabeled data. Most existing approaches focus on sampling hard negative samples in various ways. These studies are ortho
Externí odkaz:
http://arxiv.org/abs/2311.03526
Autor:
Lyu, Fuyuan, Tang, Xing, Liu, Dugang, Ma, Chen, Luo, Weihong, Chen, Liang, He, Xiuqiang, Liu, Xue
Deep sparse networks are widely investigated as a neural network architecture for prediction tasks with high-dimensional sparse features, with which feature interaction selection is a critical component. While previous methods primarily focus on how
Externí odkaz:
http://arxiv.org/abs/2310.15342
A large-scale industrial recommendation platform typically consists of multiple associated scenarios, requiring a unified click-through rate (CTR) prediction model to serve them simultaneously. Existing approaches for multi-scenario CTR prediction ge
Externí odkaz:
http://arxiv.org/abs/2306.13382