Meta-Wrapper: Differentiable Wrapping Operator for User Interest
Selection in CTR Prediction
- URL: http://arxiv.org/abs/2206.14647v1
- Date: Tue, 28 Jun 2022 03:28:15 GMT
- Title: Meta-Wrapper: Differentiable Wrapping Operator for User Interest
Selection in CTR Prediction
- Authors: Tianwei Cao, Qianqian Xu, Zhiyong Yang, and Qingming Huang
- Abstract summary: Click-through rate (CTR) prediction, whose goal is to predict the probability of the user to click on an item, has become increasingly significant in recommender systems.
Recent deep learning models with the ability to automatically extract the user interest from his/her behaviors have achieved great success.
We propose a novel approach under the framework of the wrapper method, which is named Meta-Wrapper.
- Score: 97.99938802797377
- License: http://creativecommons.org/publicdomain/zero/1.0/
- Abstract: Click-through rate (CTR) prediction, whose goal is to predict the probability
of the user to click on an item, has become increasingly significant in the
recommender systems. Recently, some deep learning models with the ability to
automatically extract the user interest from his/her behaviors have achieved
great success. In these work, the attention mechanism is used to select the
user interested items in historical behaviors, improving the performance of the
CTR predictor. Normally, these attentive modules can be jointly trained with
the base predictor by using gradient descents. In this paper, we regard user
interest modeling as a feature selection problem, which we call user interest
selection. For such a problem, we propose a novel approach under the framework
of the wrapper method, which is named Meta-Wrapper. More specifically, we use a
differentiable module as our wrapping operator and then recast its learning
problem as a continuous bilevel optimization. Moreover, we use a meta-learning
algorithm to solve the optimization and theoretically prove its convergence.
Meanwhile, we also provide theoretical analysis to show that our proposed
method 1) efficiencies the wrapper-based feature selection, and 2) achieves
better resistance to overfitting. Finally, extensive experiments on three
public datasets manifest the superiority of our method in boosting the
performance of CTR prediction.
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