Search Results for author: Ziru Liu

Found 6 papers, 3 papers with code

KuaiSim: A Comprehensive Simulator for Recommender Systems

1 code implementation NeurIPS 2023 Kesen Zhao, Shuchang Liu, Qingpeng Cai, Xiangyu Zhao, Ziru Liu, Dong Zheng, Peng Jiang, Kun Gai

For each task, KuaiSim also provides evaluation protocols and baseline recommendation algorithms that further serve as benchmarks for future research.

Reinforcement Learning (RL) Sequential Recommendation

AutoAssign+: Automatic Shared Embedding Assignment in Streaming Recommendation

no code implementations14 Aug 2023 Ziru Liu, Kecheng Chen, Fengyi Song, Bo Chen, Xiangyu Zhao, Huifeng Guo, Ruiming Tang

In the domain of streaming recommender systems, conventional methods for addressing new user IDs or item IDs typically involve assigning initial ID embeddings randomly.

Recommendation Systems

Learning Empirical Bregman Divergence for Uncertain Distance Representation

no code implementations16 Apr 2023 Zhiyuan Li, Ziru Liu, Anna Zou, Anca L. Ralescu

Deep metric learning techniques have been used for visual representation in various supervised and unsupervised learning tasks through learning embeddings of samples with deep networks.

Metric Learning

Multi-Task Recommendations with Reinforcement Learning

1 code implementation7 Feb 2023 Ziru Liu, Jiejie Tian, Qingpeng Cai, Xiangyu Zhao, Jingtong Gao, Shuchang Liu, Dayou Chen, Tonghao He, Dong Zheng, Peng Jiang, Kun Gai

To be specific, the RMTL structure can address the two aforementioned issues by (i) constructing an MTL environment from session-wise interactions and (ii) training multi-task actor-critic network structure, which is compatible with most existing MTL-based recommendation models, and (iii) optimizing and fine-tuning the MTL loss function using the weights generated by critic networks.

Multi-Task Learning Recommendation Systems +2

Multi-Task Deep Recommender Systems: A Survey

no code implementations7 Feb 2023 Yuhao Wang, Ha Tsz Lam, Yi Wong, Ziru Liu, Xiangyu Zhao, Yichao Wang, Bo Chen, Huifeng Guo, Ruiming Tang

Multi-task learning (MTL) aims at learning related tasks in a unified model to achieve mutual improvement among tasks considering their shared knowledge.

Multi-Task Learning Recommendation Systems +1

ItLnc-BXE: a Bagging-XGBoost-ensemble method with multiple features for identification of plant lncRNAs

1 code implementation1 Nov 2019 Guangyan Zhang, Ziru Liu, Jichen Dai, Zilan Yu, Shuai Liu, Wen Zhang

However, most of the existing methods are designed for lncRNAs in animal systems, and only a few methods focus on the plant lncRNA identification.

Ensemble Learning feature selection

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