A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf
.
Neural Statistics for Click-Through Rate Prediction
2022
Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
With the success of deep learning, click-through rate (CTR) predictions are transitioning from shallow approaches to deep architectures. Current deep CTR prediction usually follows the Embedding & MLP paradigm, where the model embeds categorical features into latent semantic space. This paper introduces a novel embedding technique called neural statistics that instead learns explicit semantics of categorical features by incorporating feature engineering as an innate prior into the deep
doi:10.1145/3477495.3531762
fatcat:bpl7mc3umfasjfmmivczbnck6u