Search Results for author: Zhong Ming

Found 19 papers, 8 papers with code

Augmenting Legal Judgment Prediction with Contrastive Case Relations

1 code implementation COLING 2022 Dugang Liu, Weihao Du, Lei LI, Weike Pan, Zhong Ming

Existing legal judgment prediction methods usually only consider one single case fact description as input, which may not fully utilize the information in the data such as case relations and frequency.

Decoder

Weighted self Distillation for Chinese word segmentation

1 code implementation Findings (ACL) 2022 Rian He, Shubin Cai, Zhong Ming, Jialei Zhang

Recent researches show that multi-criteria resources and n-gram features are beneficial to Chinese Word Segmentation (CWS).

Chinese Word Segmentation Segmentation

Lossless and Privacy-Preserving Graph Convolution Network for Federated Item Recommendation

no code implementations2 Dec 2024 Guowei Wu, Weike Pan, Qiang Yang, Zhong Ming

However, due to privacy constraints, the graph convolution process in existing federated recommendation methods is incomplete compared with the centralized counterpart, causing a degradation of the recommendation performance.

Graph Neural Network Privacy Preserving

Sample Enrichment via Temporary Operations on Subsequences for Sequential Recommendation

no code implementations25 Jul 2024 Shu Chen, Jinwei Luo, Weike Pan, Jiangxing Yu, Xin Huang, Zhong Ming

Sequential recommendation leverages interaction sequences to predict forthcoming user behaviors, crucial for crafting personalized recommendations.

Sequential Recommendation

A Practice-Friendly LLM-Enhanced Paradigm with Preference Parsing for Sequential Recommendation

no code implementations1 Jun 2024 Dugang Liu, Shenxian Xian, Xiaolin Lin, Xiaolian Zhang, Hong Zhu, Yuan Fang, Zhen Chen, Zhong Ming

Specifically, in the information reconstruction stage, we design a new user-level SFT task for collaborative information injection with the assistance of a pre-trained SRS model, which is more efficient and compatible with limited text information.

Sequential Recommendation

BMLP: Behavior-aware MLP for Heterogeneous Sequential Recommendation

no code implementations20 Feb 2024 Weixin Li, Yuhao Wu, Yang Liu, Weike Pan, Zhong Ming

In real recommendation scenarios, users often have different types of behaviors, such as clicking and buying.

Sequential Recommendation

Privacy-Preserving Cross-Domain Sequential Recommendation

1 code implementation27 Jan 2024 Zhaohao Lin, Weike Pan, Zhong Ming

It combines the characteristics of sequential recommender systems and cross-domain recommender systems, which can capture the dynamic preferences of users and alleviate the problem of cold-start users.

Privacy Preserving Sequential Recommendation

A Survey on Cross-Domain Sequential Recommendation

1 code implementation10 Jan 2024 Shu Chen, Zitao Xu, Weike Pan, Qiang Yang, Zhong Ming

Cross-domain sequential recommendation (CDSR) shifts the modeling of user preferences from flat to stereoscopic by integrating and learning interaction information from multiple domains at different granularities (ranging from inter-sequence to intra-sequence and from single-domain to cross-domain).

Auxiliary Learning Sequential Recommendation +1

A Survey on Multi-Behavior Sequential Recommendation

no code implementations30 Aug 2023 Xiaoqing Chen, Zhitao Li, Weike Pan, Zhong Ming

MBSR is a relatively new and worthy direction for in-depth research, which can achieve state-of-the-art recommendation through suitable modeling, and some related works have been proposed.

Information Retrieval Retrieval +2

GNN4FR: A Lossless GNN-based Federated Recommendation Framework

no code implementations25 Jul 2023 Guowei Wu, Weike Pan, Zhong Ming

Graph neural networks (GNNs) have gained wide popularity in recommender systems due to their capability to capture higher-order structure information among the nodes of users and items.

Recommendation Systems

Bounding System-Induced Biases in Recommender Systems with A Randomized Dataset

no code implementations21 Mar 2023 Dugang Liu, Pengxiang Cheng, Zinan Lin, Xiaolian Zhang, Zhenhua Dong, Rui Zhang, Xiuqiang He, Weike Pan, Zhong Ming

To bridge this gap, we study the debiasing problem from a new perspective and propose to directly minimize the upper bound of an ideal objective function, which facilitates a better potential solution to the system-induced biases.

Recommendation Systems

Self-Sampling Training and Evaluation for the Accuracy-Bias Tradeoff in Recommendation

no code implementations7 Feb 2023 Dugang Liu, Yang Qiao, Xing Tang, Liang Chen, Xiuqiang He, Weike Pan, Zhong Ming

Specifically, SSTE uses a self-sampling module to generate some subsets with different degrees of bias from the original training and validation data.

Management

An Evolutionary Multitasking Algorithm with Multiple Filtering for High-Dimensional Feature Selection

1 code implementation17 Dec 2022 Lingjie Li, Manlin Xuan, Qiuzhen Lin, Min Jiang, Zhong Ming, Kay Chen Tan

Thus, this paper devises a new EMT algorithm for FS in high-dimensional classification, which first adopts different filtering methods to produce multiple tasks and then modifies a competitive swarm optimizer to efficiently solve these related tasks via knowledge transfer.

feature selection Transfer Learning

DIWIFT: Discovering Instance-wise Influential Features for Tabular Data

1 code implementation6 Jul 2022 Dugang Liu, Pengxiang Cheng, Hong Zhu, Xing Tang, Yanyu Chen, Xiaoting Wang, Weike Pan, Zhong Ming, Xiuqiang He

Tabular data is one of the most common data storage formats behind many real-world web applications such as retail, banking, and e-commerce.

feature selection

Abstraction and Refinement: Towards Scalable and Exact Verification of Neural Networks

1 code implementation2 Jul 2022 Jiaxiang Liu, Yunhan Xing, Xiaomu Shi, Fu Song, Zhiwu Xu, Zhong Ming

Our approach is orthogonal to and can be integrated with many existing verification techniques.

PAS: A Position-Aware Similarity Measurement for Sequential Recommendation

no code implementations14 May 2022 Zijie Zeng, Jing Lin, Weike Pan, Zhong Ming, Zhongqi Lu

The common item-based collaborative filtering framework becomes a typical recommendation method when equipped with a certain item-to-item similarity measurement.

Collaborative Filtering Position +1

Diversifying Agent's Behaviors in Interactive Decision Models

no code implementations6 Mar 2022 Yinghui Pan, Hanyi Zhang, Yifeng Zeng, Biyang Ma, Jing Tang, Zhong Ming

In this article, we investigate into diversifying behaviors of other agents in the subject agent's decision model prior to their interactions.

Diversity

Zero-Bias Deep Learning for Accurate Identification of Internet of Things (IoT) Devices

1 code implementation27 Aug 2020 Yongxin Liu, Jian Wang, Jianqiang Li, Houbing Song, Thomas Yang, Shuteng Niu, Zhong Ming

In this paper, we propose an enhanced deep learning framework for IoT device identification using physical layer signals.

IoT Device Identification

A Biologically Inspired Feature Enhancement Framework for Zero-Shot Learning

no code implementations13 May 2020 Zhongwu Xie, Weipeng Cao, Xi-Zhao Wang, Zhong Ming, Jingjing Zhang, Jiyong Zhang

Most of the Zero-Shot Learning (ZSL) algorithms currently use pre-trained models as their feature extractors, which are usually trained on the ImageNet data set by using deep neural networks.

Zero-Shot Learning

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