Abstract
Learning sophisticated feature interactions is crucial for Click-Through Rate (CTR) prediction in recommender systems. Various deep CTR models follow an Embedding & Feature Interaction paradigm. The majority focus on designing network architectures in Feature Interaction module to better model feature interactions while the Embedding module, serving as a bottleneck between data and Feature Interaction module, has been overlooked. The common methods for numerical feature embedding are Normalization and Discretization. The former shares a single embedding for intra-field features and the latter transforms the features into categorical form through various discretization approaches. However, the first approach surfers from low capacity and the second one limits performance as well because the discretization rule cannot be optimized with the ultimate goal of CTR model. To fill the gap of representing numerical features, in this paper, we propose AutoDis, a framework that discretizes features in numerical fields automatically and is optimized with CTR models in an end-to-end manner. Specifically, we introduce a set of meta-embeddings for each numerical field to model the relationship among the intra-field features and propose an automatic differentiable discretization and aggregation approach to capture the correlations between the numerical features and meta-embeddings. Comprehensive experiments on two public and one industrial datasets are conducted to validate the effectiveness of AutoDis over the SOTA methods.
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URL
https://arxiv.org/abs/2012.08986
