Summary of A Novel Approach to Balance Convenience and Nutrition in Meals with Long-term Group Recommendations and Reasoning on Multimodal Recipes and Its Implementation in Beacon, by Vansh Nagpal et al.
A Novel Approach to Balance Convenience and Nutrition in Meals With Long-Term Group Recommendations and Reasoning on Multimodal Recipes and its Implementation in BEACON
by Vansh Nagpal, Siva Likitha Valluru, Kausik Lakkaraju, Nitin Gupta, Zach Abdulrahman, Andrew Davison, Biplav Srivastava
First submitted to arxiv on: 23 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a data-driven approach for personalized meal recommendations. The authors develop a solution that balances user preferences with nutritional values, cooking processes, and food constituents. They introduce goodness measures, a recipe conversion method from text to the multimodal rich recipe representation (R3) format, and learning methods using contextual bandits. Preliminary results show promising outcomes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to help people choose what to eat is being developed. This system, called BEACON, takes into account things like how healthy the food is, how much it costs, and where it comes from. The goal is to give users personalized meal suggestions that are both good for them and convenient. The authors came up with a few key ideas to make this happen, including ways to measure how “good” different foods are and converting recipes into a special format. |
