Enhancement: Store-aware shopping lists with optional AI-powered insights and recommendations

[hvaandres]

Summary

Add functionality that allows users to associate shopping list items with a specific store, combined with an optional AI-powered assistant that learns from the user’s shopping behavior to provide store suggestions, product recommendations, and smarter list organization over time.

Description

Since Starving is designed as a shopping cart / to-do list app, users would benefit from knowing where they plan to buy their items. Currently, items are store-agnostic, which makes it harder to plan shopping trips efficiently.

By allowing users to select or assign a store (e.g., Costco, Target, Walmart, Trader Joe’s) and optionally enabling AI-driven assistance, the app could help users optimize their weekly shopping experience and make more informed purchasing decisions.

Proposed Features

• Allow users to assign a store name to individual items or entire lists
• Group items by store to support multi-store shopping trips
• Persist store preferences across sessions
• Optional AI assistant that learns from the user’s shopping history

AI-Assisted Capabilities (Optional & Opt-In)

• Learn which stores users frequently shop at on a weekly or recurring basis
• Suggest stores based on the items added to the list
• Recommend additional products based on purchase history and item categories
• Provide smarter suggestions over time as more data is collected

Expected Behavior

• Users can manually select or change the store for any item
• Items can be viewed and organized by store
• AI suggestions are clearly marked and fully optional
• Users maintain full control over whether AI features are enabled

Benefits

• Improved shopping trip planning
• Reduced forgotten items across multiple stores
• Personalized recommendations based on real usage
• More intelligent and helpful shopping lists over time

Privacy & Opt-In Considerations

• AI-driven insights should be disabled by default
• Users must explicitly opt in to data-driven recommendations
• Clear messaging about what data is used and how it improves suggestions

Technical / Research Notes

• Research appropriate AI/ML or LLM models to understand item inputs and user intent
• Use historical shopping data (with consent) to improve recommendations
• Incorporate external knowledge sources (e.g., general product comparisons) to suggest better alternatives
• Ensure AI logic is modular and can evolve independently from the core app

Future Extensions

• Weekly shopping summaries and insights
• Price or value-based recommendations (where data is available)
• Store-specific product suggestions