Search Results for author: James Caverlee

Found 37 papers, 14 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

Disclosure and Mitigation of Gender Bias in LLMs

1 code implementation17 Feb 2024 Xiangjue Dong, Yibo Wang, Philip S. Yu, James Caverlee

Our experiments demonstrate that all tested LLMs exhibit explicit and/or implicit gender bias, even when gender stereotypes are not present in the inputs.

The Neglected Tails of Vision-Language Models

no code implementations23 Jan 2024 Shubham Parashar, Zhiqiu Lin, Tian Liu, Xiangjue Dong, Yanan Li, Deva Ramanan, James Caverlee, Shu Kong

We address this by using large language models (LLMs) to count the number of pretraining texts that contain synonyms of these concepts.

Retrieval Zero-Shot Learning

DALA: A Distribution-Aware LoRA-Based Adversarial Attack against Language Models

no code implementations14 Nov 2023 Yibo Wang, Xiangjue Dong, James Caverlee, Philip S. Yu

DALA considers distribution shifts of adversarial examples to improve the attack's effectiveness under detection methods.

Adversarial Attack

Unsupervised Candidate Answer Extraction through Differentiable Masker-Reconstructor Model

no code implementations19 Oct 2023 Zhuoer Wang, Yicheng Wang, Ziwei Zhu, James Caverlee

Question generation is a widely used data augmentation approach with extensive applications, and extracting qualified candidate answers from context passages is a critical step for most question generation systems.

Data Augmentation Question Generation +1

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

PromptAttack: Probing Dialogue State Trackers with Adversarial Prompts

1 code implementation7 Jun 2023 Xiangjue Dong, Yun He, Ziwei Zhu, James Caverlee

A key component of modern conversational systems is the Dialogue State Tracker (or DST), which models a user's goals and needs.

Automated Data Denoising for Recommendation

no code implementations11 May 2023 Yingqiang Ge, Mostafa Rahmani, Athirai Irissappane, Jose Sepulveda, James Caverlee, Fei Wang

In real-world scenarios, most platforms collect both large-scale, naturally noisy implicit feedback and small-scale yet highly relevant explicit feedback.

Denoising Recommendation Systems

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

Evolution of Filter Bubbles and Polarization in News Recommendation

no code implementations26 Jan 2023 Han Zhang, Ziwei Zhu, James Caverlee

However, most existing work focuses on a static setting or over a short-time window, leaving open questions about the long-term and dynamic impacts of news recommendations.

News Recommendation Recommendation Systems

Empowering Long-tail Item Recommendation through Cross Decoupling Network (CDN)

no code implementations25 Oct 2022 Yin Zhang, Ruoxi Wang, Tiansheng Yao, Xinyang Yi, Lichan Hong, James Caverlee, Ed H. Chi, Derek Zhiyuan Cheng

In this work, we aim to improve tail item recommendations while maintaining the overall performance with less training and serving cost.

Memorization Recommendation Systems +1

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

MetaBalance: Improving Multi-Task Recommendations via Adapting Gradient Magnitudes of Auxiliary Tasks

1 code implementation14 Mar 2022 Yun He, Xue Feng, Cheng Cheng, Geng Ji, Yunsong Guo, James Caverlee

Specifically, in each training iteration and adaptively for each part of the network, the gradient of an auxiliary loss is carefully reduced or enlarged to have a closer magnitude to the gradient of the target loss, preventing auxiliary tasks from being so strong that dominate the target task or too weak to help the target task.

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

Identifying Hijacked Reviews

no code implementations ACL (ECNLP) 2021 Monika Daryani, James Caverlee

Fake reviews and review manipulation are growing problems on online marketplaces globally.

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

Understanding Car-Speak: Replacing Humans in Dealerships

no code implementations6 Feb 2020 Habeeb Hooshmand, James Caverlee

A large portion of the car-buying experience in the United States involves interactions at a car dealership.

Consistency-Aware Recommendation for User-Generated ItemList Continuation

1 code implementation30 Dec 2019 Yun He, Yin Zhang, Weiwen Liu, James Caverlee

Complementary to methods that exploit specific content patterns (e. g., as in song-based playlists that rely on audio features), the proposed approach models the consistency of item lists based on human curation patterns, and so can be deployed across a wide range of varying item types (e. g., videos, images, books).

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

Tensor Completion Algorithms in Big Data Analytics

no code implementations28 Nov 2017 Qingquan Song, Hancheng Ge, James Caverlee, Xia Hu

Tensor completion is a problem of filling the missing or unobserved entries of partially observed tensors.

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