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Is your feature request related to a problem? Please describe.
Currently, users often struggle to find restaurants that match their specific tastes and dietary needs. Many existing recommendation systems rely on generic suggestions that fail to consider individual preferences, leading to dissatisfaction and missed opportunities for exploration. For instance, a user who enjoys vegan cuisine may receive suggestions for steakhouses, which can lead to frustration and a lack of trust in the system.
Describe the solution you'd like
I envision a recommendation system that collects data on user preferences, such as favorite cuisines, dietary restrictions, and previous dining history. This system would employ machine learning algorithms, like collaborative filtering and content-based filtering, to generate personalized restaurant suggestions.
Describe alternatives you've considered
Alternatives include a simple keyword-based search system that suggests restaurants based on user-inputted criteria (e.g., cuisine type or location). However, this lacks the depth of personalization and fails to adapt to changing user preferences over time.
Approach to be followed (optional)
Data Collection: Gather user data, including past restaurant visits, ratings, and preferences.
Feature Engineering: Identify relevant features for the recommendation model, such as cuisine type, user ratings, and location.
Model Selection: Explore various machine learning algorithms, including collaborative filtering, content-based filtering, and hybrid approaches.
Model Training: Train the model on collected user data and continuously refine it based on new user interactions.
User Interface: Design an intuitive interface where users can view recommendations, provide feedback, and update their preferences.
The text was updated successfully, but these errors were encountered:
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Is your feature request related to a problem? Please describe.
Currently, users often struggle to find restaurants that match their specific tastes and dietary needs. Many existing recommendation systems rely on generic suggestions that fail to consider individual preferences, leading to dissatisfaction and missed opportunities for exploration. For instance, a user who enjoys vegan cuisine may receive suggestions for steakhouses, which can lead to frustration and a lack of trust in the system.
Describe the solution you'd like
I envision a recommendation system that collects data on user preferences, such as favorite cuisines, dietary restrictions, and previous dining history. This system would employ machine learning algorithms, like collaborative filtering and content-based filtering, to generate personalized restaurant suggestions.
Describe alternatives you've considered
Alternatives include a simple keyword-based search system that suggests restaurants based on user-inputted criteria (e.g., cuisine type or location). However, this lacks the depth of personalization and fails to adapt to changing user preferences over time.
Approach to be followed (optional)
The text was updated successfully, but these errors were encountered: