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Found 8 papers, 2 papers with code
Reversible Upper Confidence Bound Algorithm to Generate Diverse Optimized Candidates
Most algorithms for the multi-armed bandit problem in reinforcement learning aimed to maximize the expected reward, which are thus useful in searching the optimized candidate with the highest reward (function value) for diverse applications (e. g., AlphaGo).
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.
Retrieval & Interaction Machine for Tabular Data Prediction
Prediction over tabular data is an essential task in many data science applications such as recommender systems, online advertising, medical treatment, etc.
AutoFT: Automatic Fine-Tune for Parameters Transfer Learning in Click-Through Rate Prediction
The field-wise transfer policy decides how the pre-trained embedding representations are frozen or fine-tuned based on the given instance from the target domain.
Dual Graph enhanced Embedding Neural Network for CTR Prediction
To solve these problems, we propose a novel module named Dual Graph enhanced Embedding, which is compatible with various CTR prediction models to alleviate these two problems.
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.
Uncovering Download Fraud Activities in Mobile App Markets
To the best of our knowledge, this is the first work that investigates the download fraud problem in mobile App markets.
