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Found 10 papers, 4 papers with code
Harnessing Multimodal Large Language Models for Multimodal Sequential Recommendation
Recent advances in Large Language Models (LLMs) have demonstrated significant potential in the field of Recommendation Systems (RSs).
PAIL: Performance based Adversarial Imitation Learning Engine for Carbon Neutral Optimization
In addition, PAIL offers meaningful interpretability for the optimization in carbon neutrality.
Cognitive Evolutionary Learning to Select Feature Interactions for Recommender Systems
We show that CELL can adaptively evolve into different models for different tasks and data, which enables practitioners to access off-the-shelf models.
Nature-Guided Cognitive Evolution for Predicting Dissolved Oxygen Concentrations in North Temperate Lakes
Using these simulated labels, we implement a multi-population cognitive evolutionary search, where models, mirroring natural organisms, adaptively evolve to select relevant feature interactions within populations for different lake types and tasks.
FREE: The Foundational Semantic Recognition for Modeling Environmental Ecosystems
This raises a fundamental question in advancing the modeling of environmental ecosystems: how to build a general framework for modeling the complex relationships amongst various environmental data over space and time?
Cognitive Evolutionary Search to Select Feature Interactions for Click-Through Rate Prediction
Inspired by natural evolution, we propose a general Cognitive EvoLutionary Search (CELS) framework, where cognitive ability refers to the malleability of organisms to orientate to the environment.
Ranked #3 on
Click-Through Rate Prediction
on Avazu
Untargeted Attack against Federated Recommendation Systems via Poisonous Item Embeddings and the Defense
Experiments on two public datasets show that ClusterAttack can effectively degrade the performance of FedRec systems while circumventing many defense methods, and UNION can improve the resistance of the system against various untargeted attacks, including our ClusterAttack.
XCrossNet: Feature Structure-Oriented Learning for Click-Through Rate Prediction
Motivated by this, we propose a novel Extreme Cross Network, abbreviated XCrossNet, which aims at learning dense and sparse feature interactions in an explicit manner.
Ranked #24 on
Click-Through Rate Prediction
on Criteo
Deep Technology Tracing for High-tech Companies
Technological change and innovation are vitally important, especially for high-tech companies.
Long-term Joint Scheduling for Urban Traffic
Recently, the traffic congestion in modern cities has become a growing worry for the residents.
