Here's the link to the code pipeline: github.com/deep-logit-d...

We can also capture hard-to-quantify characteristics such as aesthetic similarity captured by images and functional benefits mentioned in reviews. Finally, our method can be scaled very easily across categories.

👉 What are advantages of our approach? We side-step the need define which product characteristics are relevant in a given category and instead extract information from product descriptions and reviews which likely mention the characteristics most important to consumers.

👉 Does this work well? In an online choice experiment we show that our approach predicts second choices better than characteristics-based models and it predicts substitution pattern well in real-world data across 40 product categories.

👉 What do we do? We propose an approach to extract product information from unstructured text and image data that we then use as an input for a mixed logit demand model.

Please spread the word to anyone who might be interested!

The conference will bring together scholars across fields who use Machine Learning, NLP, and other tools to extract valuable insights from new types of data, including:
• unstructured data
• clickstream data
• data generated by AI.

For more information, visit: www.chicagobooth.edu/research/kil...