Shu Wu

Adaptive Pairwise Learning for Personalized Ranking with Content and Implicit Feedback

2015

Pairwise learning algorithms are a vital technique for personalized ranking with implicit feedback. They usually assume that each user is more interested in items which have been selected by the user than remaining ones. This …

Deep Graph Structure Learning for Robust Representations: A Survey

2021

Graph Neural Networks (GNNs) are widely used for analyzing graph-structured data. Most GNN methods are highly sensitive to the quality of graph structures and usually require a perfect graph structure for learning informative embeddings. However, …

Disentangled Item Representation for Recommender Systems

2021 · ACM Transactions on Intelligent Systems and Technology

Item representations in recommendation systems are expected to reveal the properties of items. Collaborative recommender methods usually represent an item as one single latent vector. Nowadays the e-commercial platforms provide various kinds of attribute information …

An Efficient Anonymous Authentication Scheme for Medical Services Based on Blockchain

2021

Telemedicine is one of the most rapidly de-veloping areas of health care in recent years. Telemedicine Information Systems (TMIS) enable physicians to pro-vide remote care over the Internet to registered patients anywhere. In this work, …

Dynamic Graph Neural Networks for Sequential Recommendation

2021 · arXiv (Cornell University)

Modeling user preference from his historical sequences is one of the core problems of sequential recommendation. Existing methods in this field are widely distributed from conventional methods to deep learning methods. However, most of them …

Uncovering Overfitting in Large Language Model Editing

2024 · arXiv (Cornell University)

Knowledge editing has been proposed as an effective method for updating and correcting the internal knowledge of Large Language Models (LLMs). However, existing editing methods often struggle with complex tasks, such as multi-hop reasoning. In …

Beyond Filtering: Adaptive Image-Text Quality Enhancement for MLLM Pretraining

2024 · arXiv (Cornell University)

Multimodal large language models (MLLMs) have made significant strides by integrating visual and textual modalities. A critical factor in training MLLMs is the quality of image-text pairs within multimodal pretraining datasets. However, $\textit {de facto}$ …

Personalized Text Generation with Contrastive Activation Steering

2025

Jinghao Zhang, Yuting Liu, Wenjie Wang, Qiang Liu, Shu Wu, Liang Wang, Tat-Seng Chua. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

SEEM: Exploiting Black-Box Text Attacks to Manipulate Tool Selection

2025 · ArXiv.org

Tool learning has emerged as a powerful auxiliary mechanism that extends the capabilities of large language models (LLMs), enabling them to address complex tasks that demand real-time relevance or high-precision operations. However, beneath this strength …

A Dynamic Recurrent Model for Next Basket Recommendation

2016

Next basket recommendation becomes an increasing concern. Most conventional models explore either sequential transaction features or general interests of users. Further, some works treat users' general interests and sequential behaviors as two totally divided matters, …

Predicting the Next Location: A Recurrent Model with Spatial and Temporal Contexts

2016 · Proceedings of the AAAI Conference on Artificial Intelligence

Spatial and temporal contextual information plays a key role for analyzing user behaviors, and is helpful for predicting where he or she will go next. With the growing ability of collecting information, more and more …

DeepStyle

2017

Visual information is an important factor in recommender systems. Some studies have been done to model user preferences for visual recommendation. Usually, an item consists of two fundamental components: style and category. Conventional methods model …

Session-Based Recommendation with Graph Neural Networks

2019 · Proceedings of the AAAI Conference on Artificial Intelligence

The problem of session-based recommendation aims to predict user actions based on anonymous sessions. Previous methods model a session as a sequence and estimate user representations besides item representations to make recommendations. Though achieved promising …

MV-RNN: A Multi-View Recurrent Neural Network for Sequential Recommendation

2018 · IEEE Transactions on Knowledge and Data Engineering

Sequential recommendation is a fundamental task for network applications, and it usually suffers from the item cold start problem due to the insufficiency of user feedbacks. There are currently three kinds of popular approaches which …

Context-Aware Sequential Recommendation

2016

Since sequential information plays an important role in modeling user behaviors, various sequential recommendation methods have been proposed. Methods based on Markov assumption are widely-used, but independently combine several most recent components. Recently, Recurrent Neural …

Deep Graph Contrastive Representation Learning

2020 · arXiv (Cornell University)

Graph representation learning nowadays becomes fundamental in analyzing graph-structured data. Inspired by recent success of contrastive methods, in this paper, we propose a novel framework for unsupervised graph representation learning by leveraging a contrastive objective …

Graph Contrastive Learning with Adaptive Augmentation

2021

Recently, contrastive learning (CL) has emerged as a successful method for unsupervised graph representation learning. Most graph CL methods first perform stochastic augmentation on the input graph to obtain two graph views and maximize the …