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Found 30 papers, 8 papers with code
SampleLLM: Optimizing Tabular Data Synthesis in Recommendations
This limitation arises from their difficulty in capturing complex distributions and understanding feature relationships from sparse and limited data, along with their inability to grasp semantic feature relations.
Cyber-physical Defense for Heterogeneous Multi-agent Systems Against Exponentially Unbounded Attacks on Signed Digraphs
The attack-resilient compensators address the EU-FDI attacks on the OL and guarantee the uniformly ultimately bounded (UUB) estimation of the leaders' states.
Defense Strategies for Autonomous Multi-agent Systems: Ensuring Safety and Resilience Under Exponentially Unbounded FDI Attacks
False data injection (FDI) attacks pose a significant threat to autonomous multi-agent systems (MASs).
Privacy-Preserving Distributed Defense Framework for DC Microgrids Against Exponentially Unbounded False Data Injection Attacks
This paper introduces a novel, fully distributed control framework for DC microgrids, enhancing resilience against exponentially unbounded false data injection (EU-FDI) attacks.
Scenario-Wise Rec: A Multi-Scenario Recommendation Benchmark
Multi Scenario Recommendation (MSR) tasks, referring to building a unified model to enhance performance across all recommendation scenarios, have recently gained much attention.
SyNeg: LLM-Driven Synthetic Hard-Negatives for Dense Retrieval
The performance of Dense retrieval (DR) is significantly influenced by the quality of negative sampling.
AltFS: Agency-light Feature Selection with Large Language Models in Deep Recommender Systems
AltFS integrates semantic reasoning from Large Language Models (LLMs) with task-specific learning from agency models.
Bridging Relevance and Reasoning: Rationale Distillation in Retrieval-Augmented Generation
The reranker and generator are two critical components in the Retrieval-Augmented Generation (i. e., RAG) pipeline, responsible for ranking relevant documents and generating responses.
Transient-Safe and Attack-Resilient Secondary Control in AC Microgrids Under Polynomially Unbounded FDI Attacks
This letter proposes a novel, fully distributed, transient-safe resilient secondary control strategies for AC microgrids, addressing unbounded false data injection (FDI) attacks on control input channels.
Prompt Tuning as User Inherent Profile Inference Machine
And employs expectation maximization to infer the embedded latent profile, minimizing textual noise by fixing the prompt template.
All Roads Lead to Rome: Unveiling the Trajectory of Recommender Systems Across the LLM Era
Recommender systems (RS) are vital for managing information overload and delivering personalized content, responding to users' diverse information needs.
LLM4MSR: An LLM-Enhanced Paradigm for Multi-Scenario Recommendation
As the demand for more personalized recommendation grows and a dramatic boom in commercial scenarios arises, the study on multi-scenario recommendation (MSR) has attracted much attention, which uses the data from all scenarios to simultaneously improve their recommendation performance.
CELA: Cost-Efficient Language Model Alignment for CTR Prediction
This model-agnostic framework can be equipped with plug-and-play textual features, with item-level alignment enhancing the utilization of external information while maintaining training and inference efficiency.
M-scan: A Multi-Scenario Causal-driven Adaptive Network for Recommendation
Furthermore, through causal graph analysis, we have discovered that the scenario itself directly influences click behavior, yet existing approaches directly incorporate data from other scenarios during the training of the current scenario, leading to prediction biases when they directly utilize click behaviors from other scenarios to train models.
LLMTreeRec: Unleashing the Power of Large Language Models for Cold-Start Recommendations
The lack of training data gives rise to the system cold-start problem in recommendation systems, making them struggle to provide effective recommendations.
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.
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.
Resilient Model-Free Asymmetric Bipartite Consensus for Nonlinear Multi-Agent Systems against DoS Attacks
In this letter, we study an unified resilient asymmetric bipartite consensus (URABC) problem for nonlinear multi-agent systems with both cooperative and antagonistic interactions under denial-of-service (DoS) attacks.
Distributed Resilient Control of DC Microgrids Under Generally Unbounded FDI Attacks
Due to the nature of distributed secondary control paradigm, DC microgrids are prone to malicious cyber-physical attacks, which could be unbounded to maximize their damage.
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.
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.
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.
Resilient Containment Control of Heterogeneous Multi-Agent Systems Against Unbounded Sensor and Actuator Attacks
To this end, we consider the resilient containment control problem of general linear heterogeneous MAS in the face of correlated and unbounded sensor attacks, as well as general unbounded actuator attacks.
3D Human Mesh Construction Leveraging Wi-Fi
In this paper, we present, Wi-Mesh, a WiFi vision-based 3D human mesh construction system.
IntTower: the Next Generation of Two-Tower Model for Pre-Ranking System
FE-Block module performs fine-grained and early feature interactions to capture the interactive signals between user and item towers explicitly and CIR module leverages a contrastive interaction regularization to further enhance the interactions implicitly.
A Comprehensive Survey of Natural Language Generation Advances from the Perspective of Digital Deception
In recent years there has been substantial growth in the capabilities of systems designed to generate text that mimics the fluency and coherence of human language.
Enhancing Explicit and Implicit Feature Interactions via Information Sharing for Parallel Deep CTR Models
The state-of-the-art deep CTR models with parallel structure (e. g., DCN) learn explicit and implicit feature interactions through independent parallel networks.
A Practical Incremental Method to Train Deep CTR Models
Deep learning models in recommender systems are usually trained in the batch mode, namely iteratively trained on a fixed-size window of training data.
Personalized Re-ranking for Improving Diversity in Live Recommender Systems
To overcome such limitation, our re-ranking model proposes a personalized DPP to model the trade-off between accuracy and diversity for each individual user.
