  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 of the pre-trained network may result in
over-fitting if the target dataset is small and the number of parameters is
large. This leads us to think of directly reusing parameters in the pre-trained
model which represent more general features learned from multiple domains.
However, the design of freezing or fine-tuning layers of parameters requires
much manual effort since the decision highly depends on the pre-trained model
and target instances. In this work, we propose an end-to-end transfer learning
framework, called Automatic Fine-Tuning (AutoFT), for CTR prediction. AutoFT
consists of a field-wise transfer policy and a layer-wise transfer policy. 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. The layer-wise transfer policy decides how the high?order
feature representations are transferred layer by layer. Extensive experiments
on two public benchmark datasets and one private industrial dataset demonstrate
that AutoFT can significantly improve the performance of CTR prediction
compared with state-of-the-art transferring approaches.
