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Found 13 papers, 8 papers with code
Uplift Modeling for Target User Attacks on Recommender Systems
UBA estimates the treatment effect on each target user and optimizes the allocation of fake user budgets to maximize the attack performance.
Lower-Left Partial AUC: An Effective and Efficient Optimization Metric for Recommendation
Compared to AUC, LLPAUC considers only the partial area under the ROC curve in the Lower-Left corner to push the optimization focus on Top-K. We provide theoretical validation of the correlation between LLPAUC and Top-K ranking metrics and demonstrate its robustness to noisy user feedback.
Enhancing Long-Term Recommendation with Bi-level Learnable Large Language Model Planning
To achieve this, we propose a Bi-level Learnable LLM Planner framework, which combines macro-learning and micro-learning through a hierarchical mechanism.
Prospect Personalized Recommendation on Large Language Model-based Agent Platform
Additionally, we prospect the evolution of Rec4Agentverse and conceptualize it into three stages based on the enhancement of the interaction and information exchange among Agent Items, Agent Recommender, and the user.
BSL: Understanding and Improving Softmax Loss for Recommendation
Loss functions steer the optimization direction of recommendation models and are critical to model performance, but have received relatively little attention in recent recommendation research.
FFHR: Fully and Flexible Hyperbolic Representation for Knowledge Graph Completion
Specifically, they suffer from two main limitations: 1) existing Graph Convolutional Network (GCN) methods in hyperbolic space rely on tangent space approximation, which would incur approximation error in representation learning, and 2) due to the lack of inner product operation definition in hyperbolic space, existing methods can only measure the plausibility of facts (links) with hyperbolic distance, which is difficult to capture complex data patterns.
On the Theories Behind Hard Negative Sampling for Recommendation
Secondly, we prove that OPAUC has a stronger connection with Top-K evaluation metrics than AUC and verify it with simulation experiments.
UperFormer: A Multi-scale Transformer-based Decoder for Semantic Segmentation
On the more challenging ADE20K dataset, our best model yields a single-scale mIoU of 50. 18, and a multi-scale mIoU of 51. 8, which is on-par with the current state-of-art model, while we drastically cut the number of FLOPs by 53. 5%.
SSformer: A Lightweight Transformer for Semantic Segmentation
It is well believed that Transformer performs better in semantic segmentation compared to convolutional neural networks.
Adaptive Random Fourier Features Kernel LMS
We propose the adaptive random Fourier features Gaussian kernel LMS (ARFF-GKLMS).
Transient Performance Analysis of the $\ell_1$-RLS
The recursive least-squares algorithm with $\ell_1$-norm regularization ($\ell_1$-RLS) exhibits excellent performance in terms of convergence rate and steady-state error in identification of sparse systems.
DisenKGAT: Knowledge Graph Embedding with Disentangled Graph Attention Network
Knowledge graph completion (KGC) has become a focus of attention across deep learning community owing to its excellent contribution to numerous downstream tasks.
BionoiNet: ligand-binding site classification with off-the-shelf deep neural network
Availability and implementation BionoiNet is implemented in Python with the source code freely available at: https://github. com/CSBG-LSU/BionoiNet.
