Aug 21, 2020 · We introduce an adaptive hashing algorithm, AutoHash, which can automatically select meaningful features to interact at high orders according to the specific ...
We introduce an adaptive hashing algorithm, AutoHash, which can automatically select meaningful features to interact at high orders according to the specific ...
To begin with, we make novel use of the Count Sketch algorithm within a DNN classifier such that high-order feature combinations can be compactly represented.
This is an AutoML approach. Experiments on three well-known public datasets demonstrate that AutoHash is significantly superior to state-of-the-art methods.
It has been a consensus that learning high-order features interaction is critical for CTR prediction. Models such as Higher-Order Factorization Machines (HOFM) ...
DCN use a Cross Net to learn both low and high order feature interaction explicitly,and use a MLP to learn feature interaction implicitly. The output of Cross ...
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Jun 10, 2024 · AutoHash: Learning higher-order feature interactions for deep CTR prediction. IEEE Transactions on Knowledge and Data Engineering, 2020 ...
AutoHash: Learning higher-order feature inter- actions for deep CTR prediction. IEEE Transactions on Knowledge and Data Engineering,. 2020. [38] Yibo Yang ...
AutoHash: Learning Higher-Order Feature Interactions for Deep CTR Prediction · Niannan XueB. Liu +6 authors. Zhenguo Li. Computer Science. IEEE Transactions on ...
The most common approach for existing models is to enumerate all possible feature interactions or to learn higher-order feature interactions by designing ...