KDD 19': Applying Deep Learning To Airbnb Search
🤗 Recommendation system paper challenge (27/50)
🤔 What problem do they solve?
They would like to apply deep learning model in search ranking.
😮 What are the challenges?
It is not easy to migrate to deep learning model. Usually we will try the most simple deep learning model but it is usually worser than the status quo model. In the other word, we are in plateau of Reality. However, we cannot stop there and need to keep trying a little complicated deep model.
😎 Overview of the models
They tried 4 models, and the first model failed but it did not stop their experiments of deep learning.
The second model, called Lambdarank NN, optimizing the NN for NDCG. In this way, it focused on pairwise and weighed more on first position of the rank. For example, improving the rank of a booked listing from position 2 to 1 would get priority over moving a booked listing from position 10 to 9.
The third model, called Decision Tree/Factorization Machine NN, taking the output of 3 models as input features into NN.
For more detail: Deep & Cross Network for Ad Click Predictions
The final model applying 2 hidden layers and mode LRUs on the model to avoid tech debts of previous models. In this model they try to keep everything simple except the output of these 2 model (Smart Pricing feature model) and listing embedding from co-view session.
🤔 So would they recommend deep learning to others?
That would be a wholehearted Yes.
And it’s not only because of the strong gains in the online performance of the model but also avoid too much manually feature engineering.
🥴 What else in this paper?
In this paper, they also discuss the failed model (what reason leading it failed and how do they solve it). Besides that, they talked about some detail feature and hyper parameter settings.
🙃 Other related blogs:
WWW’17: Visual Discovery at Pinterest
ICCV: International Conference on Computer Vision
CVPR: Conference on Computer Vision and Pattern Recognition
Top Conference Paper Challenge: