Chen Ma
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Causality-aware Graph Aggregation Weight Estimator for Popularity Debiasing in Top-K Recommendation Open
Graph-based recommender systems leverage neighborhood aggregation to generate node representations, which is highly sensitive to popularity bias, resulting in an echo effect during information propagation. Existing graph-based debiasing so…
Shapley Value-driven Data Pruning for Recommender Systems Open
Recommender systems often suffer from noisy interactions like accidental clicks or popularity bias. Existing denoising methods typically identify users' intent in their interactions, and filter out noisy interactions that deviate from the …
Revisiting Adversarial Patch Defenses on Object Detectors: Unified Evaluation, Large-Scale Dataset, and New Insights Open
Developing reliable defenses against patch attacks on object detectors has attracted increasing interest. However, we identify that existing defense evaluations lack a unified and comprehensive framework, resulting in inconsistent and inco…
Susceptibility Factor TNF-α Synergizes with Polygonum multiflorum to Drive Idiosyncratic Liver Injury in Mice by Disrupting Gut Microbiota Composition and Hepatic Metabolite Homeostasis Open
These findings suggest that TNF-α sensitization predisposes mice to PM-IDILI, potentially by disrupting gut microbial homeostasis and altering host hepatic metabolism. This research provides critical theoretical and experimental evidence r…
Reasoning in Action: MCTS-Driven Knowledge Retrieval for Large Language Models Open
Large language models (LLMs) typically enhance their performance through either the retrieval of semantically similar information or the improvement of their reasoning capabilities. However, a significant challenge remains in effectively i…
View article: Learning Binarized Representations with Pseudo-positive Sample Enhancement for Efficient Graph Collaborative Filtering
Learning Binarized Representations with Pseudo-positive Sample Enhancement for Efficient Graph Collaborative Filtering Open
Learning vectorized embeddings is fundamental to many recommender systems for user-item matching. To enable efficient online inference, representation binarization, which embeds latent features into compact binary sequences, has recently s…
View article: A Survey on Side Information-driven Session-based Recommendation: From a Data-centric Perspective
A Survey on Side Information-driven Session-based Recommendation: From a Data-centric Perspective Open
Session-based recommendation is gaining increasing attention due to its practical value in predicting the intents of anonymous users based on limited behaviors. Emerging efforts incorporate various side information to alleviate inherent da…
Hierarchical Gating Network for Cross-Domain Sequential Recommendation Open
Cross-domain sequential recommendation (CDSR) utilizes data from multiple domains to recommend the user’s next interaction based on his latest interaction sequence. Currently, many cross-domain sequential recommendation algorithms have bee…
Decision Information Meets Large Language Models: The Future of Explainable Operations Research Open
Operations Research (OR) is vital for decision-making in many industries. While recent OR methods have seen significant improvements in automation and efficiency through integrating Large Language Models (LLMs), they still struggle to prod…
Gradient-Based Multiple Robust Learning Calibration on Data Missing-Not-at-Random via Bi-Level Optimization Open
Recommendation systems (RS) have become integral to numerous digital platforms and applications, ranging from e-commerce to content streaming field. A critical problem in RS is that the ratings are missing not at random (MNAR), which is du…
Robust Uplift Modeling with Large-Scale Contexts for Real-time Marketing Open
Improving user engagement and platform revenue is crucial for online marketing platforms. Uplift modeling is proposed to solve this problem, which applies different treatments (e.g., discounts, bonus) to satisfy corresponding users. Despit…
View article: DOGR: Towards Versatile Visual Document Grounding and Referring
DOGR: Towards Versatile Visual Document Grounding and Referring Open
With recent advances in Multimodal Large Language Models (MLLMs), grounding and referring capabilities have gained increasing attention for achieving detailed understanding and flexible user interaction. However, these capabilities still r…
Supplemental figure for "AFC-ResNet18: A Novel Real-Time Image Semantic Segmentation Network for Orchard Scene Understanding" Open
Figure S is the detailed architecture of AFC-ResNet18 which is shown in figure 2 of the journal article.
View article: Supplemental figure for "AFC-ResNet18: A Novel Real-Time Image Semantic Segmentation Network for Orchard Scene Understanding"
Supplemental figure for "AFC-ResNet18: A Novel Real-Time Image Semantic Segmentation Network for Orchard Scene Understanding" Open
Figure S is the detailed architecture of AFC-ResNet18 which is shown in figure 2 of the journal article.
View article: Diffusion-based Contrastive Learning for Sequential Recommendation
Diffusion-based Contrastive Learning for Sequential Recommendation Open
Contrastive learning has been effectively utilized to enhance the training of sequential recommendation models by leveraging informative self-supervised signals. Most existing approaches generate augmented views of the same user sequence t…
SlowTrack: Increasing the Latency of Camera-Based Perception in Autonomous Driving Using Adversarial Examples Open
In Autonomous Driving (AD), real-time perception is a critical component responsible for detecting surrounding objects to ensure safe driving. While researchers have extensively explored the integrity of AD perception due to its safety and…
View article: A survey on large language model based autonomous agents
A survey on large language model based autonomous agents Open
Autonomous agents have long been a research focus in academic and industry communities. Previous research often focuses on training agents with limited knowledge within isolated environments, which diverges significantly from human learnin…
Treatment-Aware Hyperbolic Representation Learning for Causal Effect Estimation with Social Networks Open
Estimating the individual treatment effect (ITE) from observational data is a crucial research topic that holds significant value across multiple domains. How to identify hidden confounders poses a key challenge in ITE estimation. Recent s…
AFC-ResNet18: A Novel Real-Time Image Semantic Segmentation Network for Orchard Scene Understanding Open
Highlights A novel real-time image semantic segmentation network for orchards, termed AFC-ResNet18, was designed and tested. The AFC-ResNet18 model outperformed the SwiftNet network in terms of segmentation depth. The AFC-ResNet18 model ac…
Less or More From Teacher: Exploiting Trilateral Geometry For Knowledge Distillation Open
Knowledge distillation aims to train a compact student network using soft supervision from a larger teacher network and hard supervision from ground truths. However, determining an optimal knowledge fusion ratio that balances these supervi…
Towards Hybrid-grained Feature Interaction Selection for Deep Sparse Network Open
Deep sparse networks are widely investigated as a neural network architecture for prediction tasks with high-dimensional sparse features, with which feature interaction selection is a critical component. While previous methods primarily fo…
View article: Dynamic Embedding Size Search with Minimum Regret for Streaming Recommender System
Dynamic Embedding Size Search with Minimum Regret for Streaming Recommender System Open
With the continuous increase of users and items, conventional recommender\nsystems trained on static datasets can hardly adapt to changing environments.\nThe high-throughput data requires the model to be updated in a timely manner\nfor cap…
View article: Offline Imitation Learning with Variational Counterfactual Reasoning
Offline Imitation Learning with Variational Counterfactual Reasoning Open
In offline imitation learning (IL), an agent aims to learn an optimal expert behavior policy without additional online environment interactions. However, in many real-world scenarios, such as robotics manipulation, the offline dataset is c…
View article: Robustness-enhanced Uplift Modeling with Adversarial Feature Desensitization
Robustness-enhanced Uplift Modeling with Adversarial Feature Desensitization Open
Uplift modeling has shown very promising results in online marketing. However, most existing works are prone to the robustness challenge in some practical applications. In this paper, we first present a possible explanation for the above p…
Collaborative Edge Caching: a Meta Reinforcement Learning Approach with Edge Sampling Open
Current learning-based edge caching schemes usually suffer from dynamic content popularity, e.g., in the emerging short video platforms, users' request patterns shift significantly over time and across different edges. An intuitive solutio…
View article: Structure Aware Incremental Learning with Personalized Imitation Weights for Recommender Systems
Structure Aware Incremental Learning with Personalized Imitation Weights for Recommender Systems Open
Recommender systems now consume large-scale data and play a significant role in improving user experience. Graph Neural Networks (GNNs) have emerged as one of the most effective recommender system models because they model the rich relatio…