Search Results for author: Jianling Wang

Found 21 papers, 9 papers with code

Mamba4Rec: Towards Efficient Sequential Recommendation with Selective State Space Models

1 code implementation6 Mar 2024 Chengkai Liu, Jianghao Lin, Jianling Wang, Hanzhou Liu, James Caverlee

Sequential recommendation aims to estimate the dynamic user preferences and sequential dependencies among historical user behaviors.

Sequential Recommendation

Large Language Models as Data Augmenters for Cold-Start Item Recommendation

no code implementations18 Feb 2024 Jianling Wang, Haokai Lu, James Caverlee, Ed Chi, Minmin Chen

The reasoning and generalization capabilities of LLMs can help us better understand user preferences and item characteristics, offering exciting prospects to enhance recommendation systems.

Recommendation Systems

Everything Perturbed All at Once: Enabling Differentiable Graph Attacks

no code implementations29 Aug 2023 Haoran Liu, Bokun Wang, Jianling Wang, Xiangjue Dong, Tianbao Yang, James Caverlee

As powerful tools for representation learning on graphs, graph neural networks (GNNs) have played an important role in applications including social networks, recommendation systems, and online web services.

Meta-Learning Recommendation Systems +1

Learning Strong Graph Neural Networks with Weak Information

1 code implementation29 May 2023 Yixin Liu, Kaize Ding, Jianling Wang, Vincent Lee, Huan Liu, Shirui Pan

Accordingly, we propose D$^2$PT, a dual-channel GNN framework that performs long-range information propagation not only on the input graph with incomplete structure, but also on a global graph that encodes global semantic similarities.

Graph Learning

Enhancing User Personalization in Conversational Recommenders

no code implementations13 Feb 2023 Allen Lin, Ziwei Zhu, Jianling Wang, James Caverlee

Conversational recommenders are emerging as a powerful tool to personalize a user's recommendation experience.

Attribute

Quantifying and Mitigating Popularity Bias in Conversational Recommender Systems

no code implementations5 Aug 2022 Allen Lin, Jianling Wang, Ziwei Zhu, James Caverlee

Conversational recommender systems (CRS) have shown great success in accurately capturing a user's current and detailed preference through the multi-round interaction cycle while effectively guiding users to a more personalized recommendation.

Attribute Recommendation Systems

Session-based Recommendation with Hypergraph Attention Networks

no code implementations28 Dec 2021 Jianling Wang, Kaize Ding, Ziwei Zhu, James Caverlee

Session-based recommender systems aim to improve recommendations in short-term sessions that can be found across many platforms.

Session-Based Recommendations

Meta Propagation Networks for Graph Few-shot Semi-supervised Learning

1 code implementation18 Dec 2021 Kaize Ding, Jianling Wang, James Caverlee, Huan Liu

Inspired by the extensive success of deep learning, graph neural networks (GNNs) have been proposed to learn expressive node representations and demonstrated promising performance in various graph learning tasks.

Graph Learning Meta-Learning

Sequential Recommendation for Cold-start Users with Meta Transitional Learning

1 code implementation13 Jul 2021 Jianling Wang, Kaize Ding, James Caverlee

A fundamental challenge for sequential recommenders is to capture the sequential patterns of users toward modeling how users transit among items.

Few-Shot Learning Sequential Recommendation +1

Robust Graph Meta-learning for Weakly-supervised Few-shot Node Classification

no code implementations12 Jun 2021 Kaize Ding, Jianling Wang, Jundong Li, James Caverlee, Huan Liu

Graphs are widely used to model the relational structure of data, and the research of graph machine learning (ML) has a wide spectrum of applications ranging from drug design in molecular graphs to friendship recommendation in social networks.

Classification Graph Learning +4

Fairness-aware Personalized Ranking Recommendation via Adversarial Learning

1 code implementation14 Mar 2021 Ziwei Zhu, Jianling Wang, James Caverlee

This is paper is an extended and reorganized version of our SIGIR 2020~\cite{zhu2020measuring} paper.

Fairness Recommendation Systems

Popularity-Opportunity Bias in Collaborative Filtering

no code implementations WSDM 2021 Ziwei Zhu, Yun He, Xing Zhao, Yin Zhang, Jianling Wang, James Caverlee

This paper connects equal opportunity to popularity bias in implicit recommenders to introduce the problem of popularity-opportunity bias.

Collaborative Filtering

Graph Prototypical Networks for Few-shot Learning on Attributed Networks

1 code implementation23 Jun 2020 Kaize Ding, Jianling Wang, Jundong Li, Kai Shu, Chenghao Liu, Huan Liu

By constructing a pool of semi-supervised node classification tasks to mimic the real test environment, GPN is able to perform \textit{meta-learning} on an attributed network and derive a highly generalizable model for handling the target classification task.

Classification Drug Discovery +5

A Hierarchical Self-Attentive Model for Recommending User-Generated Item Lists

1 code implementation30 Dec 2019 Yun He, Jianling Wang, Wei Niu, James Caverlee

User-generated item lists are a popular feature of many different platforms.

Fairness-Aware Recommendation of Information Curators

no code implementations9 Sep 2018 Ziwei Zhu, Jianling Wang, Yin Zhang, James Caverlee

This paper highlights our ongoing efforts to create effective information curator recommendation models that can be personalized for individual users, while maintaining important fairness properties.

Fairness

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