g8keep

Personal Activity Recommendation Service

g8keep is a web- and/or mobile-based app (OR potential text and/or email-based subscription service) that generates personalized lists of things to do for users. The app utilizes machine learning to fine-tune user profiles, including interests, hobbies, budget, and schedule.

By scraping data from forums, blogs, event apps (e.g. Airbnb experiences, ra.co), op-eds, local event calendars, social media (e.g. Facebook events), and geoactivity, g8keep curates a list of tailored recommendations for the user's perfect day. The program considers factors such as time until the event, location or distance from the user's current location, commuting costs and time, and user-defined budget constraints to offer the best activity options.

In considering how to plan your perfect day, g8keep keeps into account the amount of time until the event begins, location (or distance from user's current location) and commuting costs/time, money value (as defined as a % for cost of attendance / budget) [i.e. max limit price].

The program uses machine learning to fine-tune its profile of YOU -- your interests, your hobbies, your budget, your schedule, etc. The more information provided, the better g8keep will be able to service you and recommend the best things to do.

Key Features:

1. Personalized Activity Recommendations: g8keep generates tailored activity lists based on user profiles, considering interests, hobbies, budget, and schedule.
2. Machine Learning Profile Fine-Tuning: The app uses machine learning algorithms to continuously refine user profiles for more accurate and relevant recommendations.
3. Data Scraping and Integration: g8keep scrapes data from various online sources, including forums, blogs, event apps, and social media, to gather information about local activities and events.
4. Location and Commuting Consideration: The program factors in location and commuting costs/time to suggest nearby activities that align with the user's preferences.
5. Budget Constraints: g8keep allows users to set budget limits, ensuring that recommended activities fit within their defined financial constraints.
6. User-Friendly Interface: The app provides a user-friendly interface for easy input of preferences and seamless access to personalized activity lists.

Best Coding Languages:

Python is the best coding language for this project. Python's versatility, web scraping capabilities, and machine learning libraries make it ideal for data gathering, processing, and profile fine-tuning.

JavaScript can be used for developing web-based and mobile app frontends, allowing for interactive and user-friendly interfaces.

Basic Workflow:

1. Data Scraping: The program scrapes data from forums, blogs, event apps, local event calendars, social media platforms, and other sources to gather information about activities and events.
2. User Profile Creation: Users will provide information about their interests, hobbies, budget, and schedule to create a personalized profile.
3. Machine Learning Fine-Tuning: g8keep will utilize machine learning algorithms to fine-tune user profiles, continuously improving the relevance of activity recommendations.
4. Activity Recommendation Generation: Based on the user's profile and scraped data, the program will generate a list of personalized activity recommendations.
5. Location and Commuting Analysis: The app will consider the user's location and commuting costs/time to suggest nearby activities.
6. Budget Constraints: Users can define budget constraints, and the app will filter recommendations to fit within the specified financial limits.
7. User Interaction: Users can interact with the app to explore recommended activities, adjust preferences, and find their perfect day's plan.

Basic I/O Details:

g8keep will have a user interface (web or mobile-based) where users can input their preferences and receive personalized activity lists. The program will scrape data from online sources, process it, and create user profiles for fine-tuning with machine learning algorithms.

The app will present activity recommendations to users through the interface, allowing them to explore and select activities that match their interests and fit within their schedule and budget constraints.

For the text and/or email-based subscription service, users can subscribe and receive activity recommendations via text messages or emails, eliminating the need for a dedicated app. The service will still employ the same data gathering, profile fine-tuning, and recommendation generation mechanisms to offer personalized suggestions to subscribers.