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Found 16 papers, 5 papers with code
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.
Tired of Plugins? Large Language Models Can Be End-To-End Recommenders
Recommender systems aim to predict user interest based on historical behavioral data.
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.
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.
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.
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.
