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The core of consumer purchase behavior analysis lies in building prediction models. This paper combines consumer behavior prediction with deep learning models, proposes rDNN models and KmDNN models, uses AUC and F value as evaluation indicators, implements algorithms using Python as experimental tools, derives prediction results, and conducts comparative analysis. Focusing on deep neural network models in deep learning, the performance of deep neural network models for consumer purchase behavior analysis is explored from three aspects: the underlying theory of deep neural network models, the construction and implementation of the models, and the improvement of the models, and empirical analysis is conducted through experimental results.
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Stored only when both papers are in the corpus — 10 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
Imported from OpenAlex (CC0) — nothing here is generated.