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Click-Through Rate (CTR) prediction is a pivotal task in product and contentrecommendation, where learning effective feature embeddings is of greatsignificance. However, traditional methods typically learn fixed featurerepresentations without dynamically refining feature representations accordingto the context information, leading to suboptimal performance. Some recentapproaches attempt to address this issue by learning bit-wise weights oraugmented embeddings for feature representations, but suffer from uninformativeor redundant features in the context. To tackle this problem, inspired by theGlobal Workspace Theory in conscious processing, which posits that only aspecific subset of the product features are pertinent while the rest can benoisy and even detrimental to human-click behaviors, we propose a CTR modelthat enables Dynamic Embedding Learning with Truncated Conscious Attention forCTR prediction, termed DELTA. DELTA contains two key components: (I) conscioustruncation module (CTM), which utilizes curriculum learning to apply adaptivetruncation on attention weights to select the most critical feature in thecontext; (II) explicit embedding optimization (EEO), which applies an auxiliarytask during training that directly and independently propagates the gradientfrom the loss layer to the embedding layer, thereby optimizing the embeddingexplicitly via linear feature crossing. Extensive experiments on fivechallenging CTR datasets demonstrate that DELTA achieves new state-of-artperformance among current CTR methods.

DELTA: Dynamic Embedding Learning with Truncated Conscious Attention for CTR Prediction