A three-tower embedding model fine-tuned from Fashion CLIP combined with a latent-class deep demand system captures heterogeneous consumer aesthetics, price sensitivities, and substitution patterns from large-scale retail transaction data.
Representing random utility choice models with neural networks
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InvEvolve evolves inventory policies using LLMs with RL and provides statistical safety guarantees, outperforming classical and DL methods on synthetic and real data.
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A Deep Learning Approach to Heterogeneous Consumer Aesthetics in Fast Fashion
A three-tower embedding model fine-tuned from Fashion CLIP combined with a latent-class deep demand system captures heterogeneous consumer aesthetics, price sensitivities, and substitution patterns from large-scale retail transaction data.