Peng Jiang

Sanitizable Access Control System for Secure Cloud Storage Against Malicious Data Publishers

2021 · IEEE Transactions on Dependable and Secure Computing

Cloud computing is considered as one of the most prominent paradigms in the information technology industry, since it can significantly reduce the costs of hardware and software resources in computing infrastructure. This convenience has enabled …

Building of online evaluation system based on socket protocol

2021 · Computer Science and Information Systems

As an important part of the evaluation reform, online evaluation system can effectively improve the efficiency of evaluation work, which has been paid attention by teaching institutions. The online evaluation system needs to support the …

Toward Reliable and Confidential Release for Smart Contract via ID-Based TRE

2021 · IEEE Internet of Things Journal

The concept of time release provides a new mode of sending information to the future, where the message will be available after a certainly specified period. Time-release encryption (TRE), as a promising approach, has a …

Report When Malicious: Deniable and Accountable Searchable Message-Moderation System

2022 · IEEE Transactions on Information Forensics and Security

Encrypted retrieval ensures the secure retrieval over the encrypted data without sacrificing the confidentiality. Its applications in the database systems have brought this primitive under the spotlight. Once the malicious sender sends the wrong message, …

Rethinking graph data placement for graph neural network training on multiple GPUs

2022

Graph partitioning is commonly used for dividing graph data for parallel processing. While they achieve good performance for the traditional graph processing algorithms, the existing graph partitioning methods are unsatisfactory for data-parallel GNN training on …

Two-Stage Constrained Actor-Critic for Short Video Recommendation

2023

The wide popularity of short videos on social media poses new opportunities and challenges to optimize recommender systems on the video-sharing platforms. Users sequentially interact with the system and provide complex and multi-faceted responses, including …

LEARN: Knowledge Adaptation from Large Language Model to Recommendation for Practical Industrial Application

2025 · Proceedings of the AAAI Conference on Artificial Intelligence

Contemporary recommendation systems predominantly rely on ID embedding to capture latent associations among users and items. However, this approach overlooks the wealth of semantic information embedded within textual descriptions of items, leading to suboptimal performance …

Navigate the Unknown: Enhancing LLM Reasoning with Intrinsic Motivation Guided Exploration

2025 · arXiv (Cornell University)

Reinforcement Learning (RL) has become a key approach for enhancing the reasoning capabilities of large language models. However, prevalent RL approaches like proximal policy optimization and group relative policy optimization suffer from sparse, outcome-based rewards …

CHIME: A Compressive Framework for Holistic Interest Modeling

2025 · arXiv (Cornell University)

Modeling holistic user interests is important for improving recommendation systems but is challenged by high computational cost and difficulty in handling diverse information with full behavior context. Existing search-based methods might lose critical signals during …

BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer

2019 · arXiv (Cornell University)

Modeling users' dynamic and evolving preferences from their historical behaviors is challenging and crucial for recommendation systems. Previous methods employ sequential neural networks (e.g., Recurrent Neural Network) to encode users' historical interactions from left to …

A pareto-efficient algorithm for multiple objective optimization in e-commerce recommendation

2019

Recommendation with multiple objectives is an important but difficult problem, where the coherent difficulty lies in the possible conflicts between objectives. In this case, multi-objective optimization is expected to be Pareto efficient, where no single …

Personalized re-ranking for recommendation

2019

Ranking is a core task in recommender systems, which aims at providing an ordered list of items to users. Typically, a ranking function is learned from the labeled dataset to optimize the global performance, which …

BERT4Rec

2019

Modeling users' dynamic preferences from their historical behaviors is challenging and crucial for recommendation systems. Previous methods employ sequential neural networks to encode users' historical interactions from left to right into hidden representations for making …

KuaiRand: An Unbiased Sequential Recommendation Dataset with Randomly Exposed Videos

2022 · Proceedings of the 31st ACM International Conference on Information & Knowledge Management

Recommender systems deployed in real-world applications can have inherent exposure bias, which leads to the biased logged data plaguing the researchers. A fundamental way to address this thorny problem is to collect users' interactions on …