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Found 51 papers, 18 papers with code
Tensorized Hypergraph Neural Networks
Hypergraph neural networks (HGNN) have recently become attractive and received significant attention due to their excellent performance in various domains.
IFedRec: Item-Guided Federated Aggregation for Cold-Start
To recommend cold items, existing federated recommendation models require collecting new interactions from users and retraining the model, which is time-consuming and poses a privacy threat to users' sensitive information.
Diffusion Model for GPS Trajectory Generation
With the deployment of GPS-enabled devices and data acquisition technology, the massively generated GPS trajectory data provide a core support for advancing spatial-temporal data mining research.
Domain Generalization for Mammographic Image Analysis via Contrastive Learning
Mammographic image analysis is a fundamental problem in the computer-aided diagnosis scheme, which has recently made remarkable progress with the advance of deep learning.
AutoSTL: Automated Spatio-Temporal Multi-Task Learning
To cope with the problems above, we propose an Automated Spatio-Temporal multi-task Learning (AutoSTL) method to handle multiple spatio-temporal tasks jointly.
Continuous Input Embedding Size Search For Recommender Systems
Latent factor models are the most popular backbones for today's recommender systems owing to their prominent performance.
IMF: Interactive Multimodal Fusion Model for Link Prediction
To this end, we propose a two-stage multimodal fusion framework to preserve modality-specific knowledge as well as take advantage of the complementarity between different modalities.
AutoDenoise: Automatic Data Instance Denoising for Recommendations
In the searching phase, we aim to train the policy network with the capacity of instance denoising; in the validation phase, we find out and evaluate the denoised subset of data instances selected by the trained policy network, so as to validate its denoising ability.
AutoMLP: Automated MLP for Sequential Recommendations
Sequential recommender systems aim to predict users' next interested item given their historical interactions.
User Retention-oriented Recommendation with Decision Transformer
However, deploying the DT in recommendation is a non-trivial problem because of the following challenges: (1) deficiency in modeling the numerical reward value; (2) data discrepancy between the policy learning and recommendation generation; (3) unreliable offline performance evaluation.
Denoising and Prompt-Tuning for Multi-Behavior Recommendation
In practical recommendation scenarios, users often interact with items under multi-typed behaviors (e. g., click, add-to-cart, and purchase).
Multimodal Recommender Systems: A Survey
In this paper, we will give a comprehensive survey of the MRS models, mainly from technical views.
Multi-Task Recommendations with Reinforcement Learning
To be specific, the RMTL structure can address the two aforementioned issues by (i) constructing an MTL environment from session-wise interactions and (ii) training multi-task actor-critic network structure, which is compatible with most existing MTL-based recommendation models, and (iii) optimizing and fine-tuning the MTL loss function using the weights generated by critic networks.
Exploration and Regularization of the Latent Action Space in Recommendation
To overcome this challenge, we propose a hyper-actor and critic learning framework where the policy decomposes the item list generation process into a hyper-action inference step and an effect-action selection step.
Multi-Task Deep Recommender Systems: A Survey
Multi-task learning (MTL) aims at learning related tasks in a unified model to achieve mutual improvement among tasks considering their shared knowledge.
Task-Agnostic Graph Neural Network Evaluation via Adversarial Collaboration
Existing GNN benchmarking methods for molecular representation learning focus on comparing the GNNs' performances on some node/graph classification/regression tasks on certain datasets.
RCPS: Rectified Contrastive Pseudo Supervision for Semi-Supervised Medical Image Segmentation
However, learning a robust representation from numerous unlabeled images remains challenging due to potential noise in pseudo labels and insufficient class separability in feature space, which undermines the performance of current semi-supervised segmentation approaches.
Data Augmentation on Graphs: A Technical Survey
In recent years, graph representation learning has achieved remarkable success while suffering from low-quality data problems.
A Comprehensive Survey on Trustworthy Recommender Systems
As one of the most successful AI-powered applications, recommender systems aim to help people make appropriate decisions in an effective and efficient way, by providing personalized suggestions in many aspects of our lives, especially for various human-oriented online services such as e-commerce platforms and social media sites.
"Are you okay, honey?": Recognizing Emotions among Couples Managing Diabetes in Daily Life using Multimodal Real-World Smartwatch Data
Couples generally manage chronic diseases together and the management takes an emotional toll on both patients and their romantic partners.
TBI-GAN: An Adversarial Learning Approach for Data Synthesis on Traumatic Brain Segmentation
To address these issues, we propose a novel medical image inpainting model named TBI-GAN to synthesize TBI MR scans with paired brain label maps.
A Privacy-Aware Graph Contrastive Learning Method in Federated Settings
In this paper, we investigate how to implement differential privacy on graph edges and observe the performances decreasing in the experiments.
Knowledge-enhanced Black-box Attacks for Recommendations
Recent studies have shown that deep neural networks-based recommender systems are vulnerable to adversarial attacks, where attackers can inject carefully crafted fake user profiles (i. e., a set of items that fake users have interacted with) into a target recommender system to achieve malicious purposes, such as promote or demote a set of target items.
Generating Counterfactual Hard Negative Samples for Graph Contrastive Learning
In this paper, we propose a novel method to utilize \textbf{C}ounterfactual mechanism to generate artificial hard negative samples for \textbf{G}raph \textbf{C}ontrastive learning, namely \textbf{CGC}, which has a different perspective compared to those sampling-based strategies.
SC-Ques: A Sentence Completion Question Dataset for English as a Second Language Learners
Sentence completion (SC) questions present a sentence with one or more blanks that need to be filled in, three to five possible words or phrases as options.
Spatial Attention-based Implicit Neural Representation for Arbitrary Reduction of MRI Slice Spacing
Magnetic resonance (MR) images collected in 2D clinical protocols typically have large inter-slice spacing, resulting in high in-plane resolution and reduced through-plane resolution.
MLP4Rec: A Pure MLP Architecture for Sequential Recommendations
In addition, most existing works assume that such sequential dependencies exist solely in the item embeddings, but neglect their existence among the item features.
AutoField: Automating Feature Selection in Deep Recommender Systems
Thereby, feature selection is a critical process in developing deep learning-based recommender systems.
A Comprehensive Survey on Automated Machine Learning for Recommendations
Deep recommender systems (DRS) are critical for current commercial online service providers, which address the issue of information overload by recommending items that are tailored to the user's interests and preferences.
Building a 3-Player Mahjong AI using Deep Reinforcement Learning
In this paper, we present Meowjong, an AI for Sanma using deep reinforcement learning.
Prior Attention Network for Multi-Lesion Segmentation in Medical Images
The proposed network can be regarded as a universal solution to multi-lesion segmentation in both 2D and 3D tasks.
Graph Trend Filtering Networks for Recommendations
The key of recommender systems is to predict how likely users will interact with items based on their historical online behaviors, e. g., clicks, add-to-cart, purchases, etc.
AutoLoss: Automated Loss Function Search in Recommendations
Unlike existing algorithms, the proposed controller can adaptively generate the loss probabilities for different data examples according to their varied convergence behaviors.
Automated Self-Supervised Learning for Graphs
Then we propose the AutoSSL framework which can automatically search over combinations of various self-supervised tasks.
Data-Efficient Reinforcement Learning for Malaria Control
This work introduces a practical, data-efficient policy learning method, named Variance-Bonus Monte Carlo Tree Search~(VB-MCTS), which can copy with very little data and facilitate learning from scratch in only a few trials.
D2A U-Net: Automatic Segmentation of COVID-19 Lesions from CT Slices with Dilated Convolution and Dual Attention Mechanism
In this paper we propose a dilated dual attention U-Net (D2A U-Net) for COVID-19 lesion segmentation in CT slices based on dilated convolution and a novel dual attention mechanism to address the issues above.
Towards Long-term Fairness in Recommendation
We focus on the fairness of exposure of items in different groups, while the division of the groups is based on item popularity, which dynamically changes over time in the recommendation process.
Watermarking Graph Neural Networks by Random Graphs
Many learning tasks require us to deal with graph data which contains rich relational information among elements, leading increasing graph neural network (GNN) models to be deployed in industrial products for improving the quality of service.
Neural Interactive Collaborative Filtering
Therefore, the proposed exploration policy, to balance between learning the user profile and making accurate recommendations, can be directly optimized by maximizing users' long-term satisfaction with reinforcement learning.
Memory-efficient Embedding for Recommendations
Specifically, we first proposed an end-to-end differentiable framework that can calculate the weights over various dimensions for feature fields in a soft and continuous manner with an AutoML based optimization algorithm; then we derive a hard and discrete embedding component architecture according to the maximal weights and retrain the whole recommender framework.
Attacking Black-box Recommendations via Copying Cross-domain User Profiles
In this work, we present our framework CopyAttack, which is a reinforcement learning based black-box attack method that harnesses real users from a source domain by copying their profiles into the target domain with the goal of promoting a subset of items.
Jointly Learning to Recommend and Advertise
Online recommendation and advertising are two major income channels for online recommendation platforms (e. g. e-commerce and news feed site).
AutoEmb: Automated Embedding Dimensionality Search in Streaming Recommendations
Deep learning based recommender systems (DLRSs) often have embedding layers, which are utilized to lessen the dimensionality of categorical variables (e. g. user/item identifiers) and meaningfully transform them in the low-dimensional space.
A Semi-Supervised Stable Variational Network for Promoting Replier-Consistency in Dialogue Generation
However, the sampled information from latent space usually becomes useless due to the KL divergence vanishing issue, and the highly abstractive global variables easily dilute the personal features of replier, leading to a non replier-specific response.
DEAR: Deep Reinforcement Learning for Online Advertising Impression in Recommender Systems
However, most RL-based advertising algorithms focus on optimizing ads' revenue while ignoring the possible negative influence of ads on user experience of recommended items (products, articles and videos).
Toward Simulating Environments in Reinforcement Learning Based Recommendations
Thus, it calls for a user simulator that can mimic real users' behaviors where we can pre-train and evaluate new recommendation algorithms.
Whole-Chain Recommendations
With the recent prevalence of Reinforcement Learning (RL), there have been tremendous interests in developing RL-based recommender systems.
Multi-agent Reinforcement Learning
Recommendation Systems
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Deep reinforcement learning for search, recommendation, and online advertising: a survey
Search, recommendation, and online advertising are the three most important information-providing mechanisms on the web.
Deep Reinforcement Learning for Page-wise Recommendations
In particular, we propose a principled approach to jointly generate a set of complementary items and the corresponding strategy to display them in a 2-D page; and propose a novel page-wise recommendation framework based on deep reinforcement learning, DeepPage, which can optimize a page of items with proper display based on real-time feedback from users.
Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning
Users' feedback can be positive and negative and both types of feedback have great potentials to boost recommendations.
Deep Reinforcement Learning for List-wise Recommendations
Recommender systems play a crucial role in mitigating the problem of information overload by suggesting users' personalized items or services.
