ML backend for Scenic Route Recommender AIM Labs Spring 2023

• Large training data files are stored in ./data, but are obviously not included in git.
  • Run ./data/downloader.py to get these data files.
• Similarly, models are stored in ./models, but not in git

Resources:

• Google Maps Python API
  • Useful Examples
  • Python Docs
• Env Variables
• Doc2Vec
• TripAdvisor API
• Instagram API
• Video to trip?
• Concatenate all adjectives (part of speech labeling)
• not adjectives but nouns: hills, lakes, etc.
  • Because a lot of "good" adjectives are the same

Efficiency

• waypoints - come up with an algorithm that selects a set of waypoitns that does not have overlap
• waypoints - generate new points beyond the route to perform more nearby searches
• filtering - filter based on total reviews/rating?
• reviews - google about (editorial summary), reviews, wikipedia (free), tripadvisor?
• average word2vec
• get routes through top-performing detours

Pip packages (requirements.txt included):

• python-dotenv
• googlemaps
• open-ai
• gensim

Meeting with Zack:

• Use Doc2Vec as a first pass preprocessing to filter thousands of locations -> few dozen
  • cosine similarity
• Use ChatGPT to then compare the keywords to the reviews/ information about the few dozen locations to pick the best one.
• ChatGPT API
  • For every prompt, to get ChatGPT to respond in a format that you want, use prompt engineering.
  • Be very specific with the prompt ("treat ChatGPT like a 5 year old").
  • Describe the general framework/ give instructions.
  • Ex:
• Models on HuggingFace