Search Results for author:
Found 4 papers, 1 papers with code
Continual Learning for CTR Prediction: A Hybrid Approach
To address this problem, we formulate CTR prediction as a continual learning task and propose COLF, a hybrid COntinual Learning Framework for CTR prediction, which has a memory-based modular architecture that is designed to adapt, learn and give predictions continuously when faced with non-stationary drifting click data streams.
Deep Position-wise Interaction Network for CTR Prediction
Click-through rate (CTR) prediction plays an important role in online advertising and recommender systems.
A Deep Spatio-Temporal Fuzzy Neural Network for Passenger Demand Prediction
In spite of its importance, passenger demand prediction is a highly challenging problem, because the demand is simultaneously influenced by the complex interactions among many spatial and temporal factors and other external factors such as weather.
Bandit Learning with Implicit Feedback
Implicit feedback, such as user clicks, although abundant in online information service systems, does not provide substantial evidence on users' evaluation of system's output.
