Search Results for author:
Found 94 papers, 39 papers with code
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.
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 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.
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.
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
+1
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.
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).
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.
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.
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).
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.
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.
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.
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.
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.
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.
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.
Automated Self-Supervised Learning for Graphs
Then we propose the AutoSSL framework which can automatically search over combinations of various self-supervised tasks.
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.
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.
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.
Building a 3-Player Mahjong AI using Deep Reinforcement Learning
In this paper, we present Meowjong, an AI for Sanma using deep reinforcement learning.
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.
AutoField: Automating Feature Selection in Deep Recommender Systems
Thereby, feature selection is a critical process in developing deep learning-based recommender systems.
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.
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.
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.
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.
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.
Mitigating the Performance Sacrifice in DP-Satisfied Federated Settings through Graph Contrastive Learning
Currently, graph learning models are indispensable tools to help researchers explore graph-structured data.
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.
"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.
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.
Data Augmentation on Graphs: A Technical Survey
In recent years, graph representation learning has achieved remarkable success while suffering from low-quality data problems.
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.
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.
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.
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.
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.
Multimodal Recommender Systems: A Survey
In this paper, we will give a comprehensive survey of the MRS models, mainly from technical views.
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).
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.
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.
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.
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.
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.
Domain Generalization for Mammographic Image Analysis with Contrastive Learning
The training of an efficacious deep learning model requires large data with diverse styles and qualities.
Tensorized Hypergraph Neural Networks
THNN is a faithful hypergraph modeling framework through high-order outer product feature message passing and is a natural tensor extension of the adjacency-matrix-based graph neural networks.
Hybrid Graph: A Unified Graph Representation with Datasets and Benchmarks for Complex Graphs
Additionally, though many Graph Neural Networks (GNNs) have been proposed for representation learning on higher-order graphs, they are usually only evaluated on simple graph datasets.
OpenSiteRec: An Open Dataset for Site Recommendation
As a representative information retrieval task, site recommendation, which aims at predicting the optimal sites for a brand or an institution to open new branches in an automatic data-driven way, is beneficial and crucial for brand development in modern business.
Recommender Systems in the Era of Large Language Models (LLMs)
As a result, recent studies have attempted to harness the power of LLMs to enhance recommender systems.
CT-based Subchondral Bone Microstructural Analysis in Knee Osteoarthritis via MR-Guided Distillation Learning
The regressed trabecular parameters were further used for knee osteoarthritis classification.
AutoAssign+: Automatic Shared Embedding Assignment in Streaming Recommendation
In the domain of streaming recommender systems, conventional methods for addressing new user IDs or item IDs typically involve assigning initial ID embeddings randomly.
Will More Expressive Graph Neural Networks do Better on Generative Tasks?
Moreover, we show that GCPN and GraphAF with advanced GNNs can achieve state-of-the-art results across 17 other non-GNN-based graph generative approaches, such as variational autoencoders and Bayesian optimisation models, on the proposed molecular generative objectives (DRD2, Median1, Median2), which are impor- tant metrics for de-novo molecular design.
AdLER: Adversarial Training with Label Error Rectification for One-Shot Medical Image Segmentation
To address these issues, we propose a novel one-shot medical image segmentation method with adversarial training and label error rectification (AdLER), with the aim of improving the diversity of generated data and correcting label errors to enhance segmentation performance.
Scenario-Aware Hierarchical Dynamic Network for Multi-Scenario Recommendation
To address these limitations, we propose a Scenario-Aware Hierarchical Dynamic Network for Multi-Scenario Recommendations (HierRec), which perceives implicit patterns adaptively and conducts explicit and implicit scenario modeling jointly.
HAMUR: Hyper Adapter for Multi-Domain Recommendation
Secondly, due to the distribution differences among domains, the utilization of static parameters in existing methods limits their flexibility to adapt to diverse domains.
PromptST: Prompt-Enhanced Spatio-Temporal Multi-Attribute Prediction
We devise a spatio-temporal transformer and a parameter-sharing training scheme to address the common knowledge among different spatio-temporal attributes.
Rethinking Sensors Modeling: Hierarchical Information Enhanced Traffic Forecasting
Then, we generate representative and common spatio-temporal patterns as global nodes to reflect a global dependency between sensors and provide auxiliary information for spatio-temporal dependency learning.
KuaiSim: A Comprehensive Simulator for Recommender Systems
For each task, KuaiSim also provides evaluation protocols and baseline recommendation algorithms that further serve as benchmarks for future research.
Diffusion Augmentation for Sequential Recommendation
However, sequential recommendation often faces the problem of data sparsity, which widely exists in recommender systems.
MLPST: MLP is All You Need for Spatio-Temporal Prediction
To accomplish the above goals, we propose an intuitive and novel framework, MLPST, a pure multi-layer perceptron architecture for traffic prediction.
EasyGen: Easing Multimodal Generation with a Bidirectional Conditional Diffusion Model and LLMs
We present EasyGen, an efficient model designed to enhance multimodal understanding and generation by harnessing the capabilities of diffusion models and large language models (LLMs).
MOELoRA: An MOE-based Parameter Efficient Fine-Tuning Method for Multi-task Medical Applications
Additionally, we propose a task-motivated gate function for all MOELoRA layers that can regulate the contributions of each expert and generate distinct parameters for various tasks.
Enhancing Traffic Prediction with Learnable Filter Module
Our approach focuses on enhancing the quality of the input data for traffic prediction models, which is a critical yet often overlooked aspect in the field.
Embedding in Recommender Systems: A Survey
This survey covers embedding methods like collaborative filtering, self-supervised learning, and graph-based techniques.
MILL: Mutual Verification with Large Language Models for Zero-Shot Query Expansion
While existing methods expand queries using retrieved or generated contextual documents, each approach has notable limitations.
Adversarial Batch Inverse Reinforcement Learning: Learn to Reward from Imperfect Demonstration for Interactive Recommendation
In this research, we focus on the problem of learning to reward (LTR), which is fundamental to reinforcement learning.
A General Neural Causal Model for Interactive Recommendation
Mitigation of the survivor bias is achieved though counterfactual consistency.
On the Opportunities of Green Computing: A Survey
To tackle the challenges of computing resources and environmental impact of AI, Green Computing has become a hot research topic.
MeLo: Low-rank Adaptation is Better than Fine-tuning for Medical Image Diagnosis
By fixing the weight of ViT models and only adding small low-rank plug-ins, we achieve competitive results on various diagnosis tasks across different imaging modalities using only a few trainable parameters.
Uni-COAL: A Unified Framework for Cross-Modality Synthesis and Super-Resolution of MR Images
Cross-modality synthesis (CMS), super-resolution (SR), and their combination (CMSR) have been extensively studied for magnetic resonance imaging (MRI).
Federated Knowledge Graph Completion via Latent Embedding Sharing and Tensor Factorization
To address these issues, we propose a novel method, i. e., Federated Latent Embedding Sharing Tensor factorization (FLEST), which is a novel approach using federated tensor factorization for KG completion.
E4SRec: An Elegant Effective Efficient Extensible Solution of Large Language Models for Sequential Recommendation
Furthermore, practical ID-based recommendation strategies, reliant on a huge number of unique identities (IDs) to represent users and items, have gained prominence in real-world recommender systems due to their effectiveness and efficiency.
Large Multimodal Model Compression via Efficient Pruning and Distillation at AntGroup
In our research, we constructed a dataset, the Multimodal Advertisement Audition Dataset (MAAD), from real-world scenarios within Alipay, and conducted experiments to validate the reliability of our proposed strategy.
A Unified Framework for Multi-Domain CTR Prediction via Large Language Models
Click-Through Rate (CTR) prediction is a crucial task in online recommendation platforms as it involves estimating the probability of user engagement with advertisements or items by clicking on them.
Agent4Ranking: Semantic Robust Ranking via Personalized Query Rewriting Using Multi-agent LLM
To be specific, we use Chain of Thought (CoT) technology to utilize Large Language Models (LLMs) as agents to emulate various demographic profiles, then use them for efficient query rewriting, and we innovate a robust Multi-gate Mixture of Experts (MMoE) architecture coupled with a hybrid loss function, collectively strengthening the ranking models' robustness.
Large Language Models for Generative Information Extraction: A Survey
Information extraction (IE) aims to extract structural knowledge (such as entities, relations, and events) from plain natural language texts.
An Open and Comprehensive Pipeline for Unified Object Grounding and Detection
Grounding-DINO is a state-of-the-art open-set detection model that tackles multiple vision tasks including Open-Vocabulary Detection (OVD), Phrase Grounding (PG), and Referring Expression Comprehension (REC).
Cumulative Distribution Function based General Temporal Point Processes
Our contributions encompass the introduction of a pioneering CDF-based TPP model, the development of a methodology for incorporating past event information into future event prediction, and empirical validation of CuFun's effectiveness through extensive experimentation on synthetic and real-world datasets.
EASRec: Elastic Architecture Search for Efficient Long-term Sequential Recommender Systems
Our research addresses the computational and resource inefficiencies that current Sequential Recommender Systems (SRSs) suffer from.
Large Language Model Distilling Medication Recommendation Model
In this paper, we introduce a novel approach called Large Language Model Distilling Medication Recommendation (LEADER).
Rethinking Large Language Model Architectures for Sequential Recommendations
This design stems from our empirical observation that beam search decoding is ultimately unnecessary for sequential recommendations.
Adversarial Curriculum Graph Contrastive Learning with Pair-wise Augmentation
To address this challenge, we propose an innovative framework: Adversarial Curriculum Graph Contrastive Learning (ACGCL), which capitalizes on the merits of pair-wise augmentation to engender graph-level positive and negative samples with controllable similarity, alongside subgraph contrastive learning to discern effective graph patterns therein.
Enhancing Real-World Complex Network Representations with Hyperedge Augmentation
These methods cannot fully address the complexities of real-world large-scale networks that often involve higher-order node relations beyond only being pairwise.
BiVRec: Bidirectional View-based Multimodal Sequential Recommendation
The integration of multimodal information into sequential recommender systems has attracted significant attention in recent research.
Editing Factual Knowledge and Explanatory Ability of Medical Large Language Models
In this paper, we propose two model editing studies and validate them in the medical domain: (1) directly editing the factual medical knowledge and (2) editing the explanations to facts.
Multi-perspective Improvement of Knowledge Graph Completion with Large Language Models
Knowledge graph completion (KGC) is a widely used method to tackle incompleteness in knowledge graphs (KGs) by making predictions for missing links.
The 2nd Workshop on Recommendation with Generative Models
The rise of generative models has driven significant advancements in recommender systems, leaving unique opportunities for enhancing users' personalized recommendations.
SSDRec: Self-Augmented Sequence Denoising for Sequential Recommendation
In the second stage, we devise a self-augmentation module to augment sequences to alleviate OUPs.
ERASE: Benchmarking Feature Selection Methods for Deep Recommender Systems
Secondly, the existing literature's lack of detailed analysis on selection attributes, based on large-scale datasets and a thorough comparison among selection techniques and DRS backbones, restricts the generalizability of findings and impedes deployment on DRS.
Tired of Plugins? Large Language Models Can Be End-To-End Recommenders
Recommender systems aim to predict user interest based on historical behavioral data.
Sequential Recommendation for Optimizing Both Immediate Feedback and Long-term Retention
In the landscape of Recommender System (RS) applications, reinforcement learning (RL) has recently emerged as a powerful tool, primarily due to its proficiency in optimizing long-term rewards.
