Zhirong Liu

AutoFT: Automatic Fine-Tune for Parameters Transfer Learning in Click-Through Rate Prediction

2021 · arXiv (Cornell University)

Recommender systems are often asked to serve multiple recommendation scenarios or domains. Fine-tuning a pre-trained CTR model from source domains and adapting it to a target domain allows knowledge transferring. However, optimizing all the parameters …

Retrieval & Interaction Machine for Tabular Data Prediction

2021 · arXiv (Cornell University)

Prediction over tabular data is an essential task in many data science applications such as recommender systems, online advertising, medical treatment, etc. Tabular data is structured into rows and columns, with each row as a …

Enhancing Explicit and Implicit Feature Interactions via Information Sharing for Parallel Deep CTR Models

2021

Effectively modeling feature interactions is crucial for CTR prediction in industrial recommender systems. The state-of-the-art deep CTR models with parallel structure (e.g., DCN) learn explicit and implicit feature interactions through independent parallel networks. However, these …