Inspiration
We were inspired by the urgency of climate change and Migros' commitment to promoting sustainable shopping.
What it does
MigroChip uses AI to recommend sustainable products to shoppers. It analyzes user purchase data to suggest greener alternatives that are not more than 15% pricier than their original choices.
How we built it
We leveraged Migros' Product and Cumulus purchase datasets to implement a collaborative and Content-based recommendation system. Product sustainability was scored based on CO2 emissions, seasonality, and Mcheck value. This information is then synthesized to produce a 'sustainability score', enabling our system to suggest greener alternatives that align with a user's buying preferences, ensuring a fair price. Additionally our app provides a chatbot recommending recipes.
Challenges we ran into
Balancing the accuracy of recommendations with product affordability and ensuring the sustainability metrics were consistently reliable.
Accomplishments that we're proud of
Successfully integrating the recommendation systems into shopping recommendations and the chatbot, making green choices accessible and straightforward for shoppers.
What we learned
The significance of user-centric design in encouraging sustainable shopping habits.
What's next for MigroChip
Expanding the attributes of products covered (categories, metadata), refining our sustainability metrics, and integrating real-time user feedback to continuously improve recommendations.
Built With
- analysis
- google-cloud
- ml
- python
- recommendation

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