Xiaofeng Gao

Seq2Bubbles

2021

User behavior sequences contain rich information about user interests and are exploited to predict user's future clicking in sequential recommendation. Existing approaches, especially recently proposed deep learning models, often embed a sequence of clicked items …

LAMEE: A Light All-MLP Framework for TimeSeries Prediction Empowering Recommendations

2023 · Research Square

Abstract Exogenous variables, unrelated to the recommendation system itself, can significantly enhance its performance. Therefore, integrating these time-evolving exogenous variables into a time series and conducting time series predictions can maximize the potential of recommendation …

SCRIPT: Sequential Cross-Meta-Information Recommendation in Pretrain and Prompt Paradigm

2023

Existing online advertising systems employ separate models for each task and site, resulting in a large number of models that require significant computing power and human effort to train and deploy. Moreover, separate models have …

FEDGE: An Interference-Aware QoS Prediction Framework for Black-Box Scenario in IaaS Clouds with Domain Generalization

2024

Public cloud providers embrace multi-tenancy as a strategy to enhance the utilization and efficiency of resources. However, co-located virtual machines (VMs) suffer from qualityof-service (QoS) degradation caused by shared resource interference. Existing solutions for predicting …

Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systems

2019

Social recommendation leverages social information to solve data sparsity and cold-start problems in traditional collaborative filtering methods. However, most existing models assume that social effects from friend users are static and under the forms of …