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Recommender system for
restaurant menu app

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[vc_column_text]The project was implemented for a startup based in the US, who was building a mobile app for personalized restaurant menu recommendations.[/vc_column_text]

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About this project

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Project Description:

As part of the project, we’ve scraped data about meals and menus from the internet. More than 7 million menu items from around 40,000 restaurants in the USA.

Feature extraction techniques were implemented in order to transform semi-structured data into a more useful representation. A personalized content-based menu recommendation system was created.

We’ve incorporated filtering techniques, specifically to deal with the cold start problem.

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Technologies:

• spaCy, PyTorch.
• Scrapy framework to collect data from the internet.

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