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Macau Hidden Bites
Macau Hidden Bites is a Macau food recommendation agent that matches user budget, weather, and wait preferences with four-dimensional weights.
Macau Hidden Bites is an AI agent designed to recommend Macau restaurants based on user budget, group size, weather, and wait tolerance. The agent outputs a set of four traceable weights covering taste match, quality stability, convenience, and value for money. It features a structured knowledge base of fifty fictional restaurants, a recommendation engine combining Thompson Sampling and StackedPareto models, and a full evaluation pipeline with five-fold cross-validation. The project includes simulation data for five thousand users and one hundred thousand interactions to validate recommendation links and weight recovery methods. It is built as a student competition entry by team UM-Zhuoguang and deploys locally as a QwenPaw custom skill.
Macau Hidden Bites at a glance
Key features
- Structured knowledge base with fifty fictional Macau restaurants
- Thompson Sampling exposure allocation engine
- StackedPareto weight fusion model
- Five-fold cross-validation evaluation pipeline
- Simulation data generation for users and interactions
- Local deployment as a QwenPaw custom skill
Use it for
- Recommend Macau restaurants based on budget and weather
- Evaluate recommendation strategies using simulated user interactions
- Run local food recommendation tasks via custom agent skills
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