1 code implementation • 31 May 2024 • Ding Zou, Wei Wei, Feida Zhu, Chuanyu Xu, Tao Zhang, Chengfu Huo
However, simply integrating KG into recommendation usually brings in negative feedback in industry, due to the ignorance of the following two factors: i) users' multiple intents, which involve diverse nodes in KG.
no code implementations • 26 Oct 2023 • Ding Zou, Wei Lu, Zhibo Zhu, Xingyu Lu, Jun Zhou, Xiaojin Wang, KangYu Liu, Haiqing Wang, Kefan Wang, Renen Sun
The reactive module provides a self-tuning estimator of CPU utilization to the optimization model.
1 code implementation • 22 Aug 2022 • Ding Zou, Wei Wei, Ziyang Wang, Xian-Ling Mao, Feida Zhu, Rui Fang, Dangyang Chen
Specifically, we first construct local and non-local graphs for user/item in KG, exploring more KG facts for KGR.
1 code implementation • 19 Apr 2022 • Ding Zou, Wei Wei, Xian-Ling Mao, Ziyang Wang, Minghui Qiu, Feida Zhu, Xin Cao
Different from traditional contrastive learning methods which generate two graph views by uniform data augmentation schemes such as corruption or dropping, we comprehensively consider three different graph views for KG-aware recommendation, including global-level structural view, local-level collaborative and semantic views.
1 code implementation • 23 Feb 2022 • Sen Zhao, Wei Wei, Ding Zou, Xianling Mao
Specifically, MIDGN disentangles the user's intents from two different perspectives, respectively: 1) In the global level, MIDGN disentangles the user's intent coupled with inter-bundle items; 2) In the Local level, MIDGN disentangles the user's intent coupled with items within each bundle.