Search Results for author: James Caverlee

Found 15 papers, 8 papers with code

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.

Weakly-supervised Graph Meta-learning for 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 +3

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 Frame +1

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.

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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