Search Results for author: ZhiCheng He

Found 6 papers, 3 papers with code

Compressed Interaction Graph based Framework for Multi-behavior Recommendation

1 code implementation4 Mar 2023 Wei Guo, Chang Meng, Enming Yuan, ZhiCheng He, Huifeng Guo, Yingxue Zhang, Bo Chen, Yaochen Hu, Ruiming Tang, Xiu Li, Rui Zhang

However, it is challenging to explore multi-behavior data due to the unbalanced data distribution and sparse target behavior, which lead to the inadequate modeling of high-order relations when treating multi-behavior data ''as features'' and gradient conflict in multitask learning when treating multi-behavior data ''as labels''.

Multi-Task Learning

A Survey on User Behavior Modeling in Recommender Systems

no code implementations22 Feb 2023 ZhiCheng He, Weiwen Liu, Wei Guo, Jiarui Qin, Yingxue Zhang, Yaochen Hu, Ruiming Tang

Besides, we elaborate on the industrial practices of UBM methods with the hope of providing insights into the application value of existing UBM solutions.

Recommendation Systems

Disentangling Past-Future Modeling in Sequential Recommendation via Dual Networks

1 code implementation26 Oct 2022 Hengyu Zhang, Enming Yuan, Wei Guo, ZhiCheng He, Jiarui Qin, Huifeng Guo, Bo Chen, Xiu Li, Ruiming Tang

Sequential recommendation (SR) plays an important role in personalized recommender systems because it captures dynamic and diverse preferences from users' real-time increasing behaviors.

Disentanglement Information Retrieval +1

MISS: Multi-Interest Self-Supervised Learning Framework for Click-Through Rate Prediction

no code implementations30 Nov 2021 Wei Guo, Can Zhang, ZhiCheng He, Jiarui Qin, Huifeng Guo, Bo Chen, Ruiming Tang, Xiuqiang He, Rui Zhang

With the help of two novel CNN-based multi-interest extractors, self-supervision signals are discovered with full considerations of different interest representations (point-wise and union-wise), interest dependencies (short-range and long-range), and interest correlations (inter-item and intra-item).

Click-Through Rate Prediction Contrastive Learning +3

Quality and Computation Time in Optimization Problems

no code implementations20 Nov 2021 ZhiCheng He

In this paper, we investigate the quality and computation time of optimization algorithms in optimization problems, instead of the one-for-all evaluation of quality.

Bayesian Optimization Evolutionary Algorithms

Mining Functional Modules by Multiview-NMF of Phenome-Genome Association

1 code implementation11 May 2017 YaoGong Zhang, YingJie Xu, Xin Fan, YuXiang Hong, Jiahui Liu, ZhiCheng He, YaLou Huang, MaoQiang Xie

In particular, the hierarchical structure of ontology has not been sufficiently utilized in clustering genes while functionally related genes are consistently associated with phenotypes on the same path in the phenotype ontology.


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