Users want to find Diner's club merchants, but have no metadata to inform their decision, unless they go and get it themselves.
Bring the best available metadata on Diner's club merchants to the user in the web app.
Using a ML model, we create a curated list of merchants for each user and sort that list
according to each individual user's predicted preferences.
We use the following APIs:
- Discover City Guides
- Yelp
- Handling pagination in TamperMonkey script
- Yelp API timeout preventing model application to ALL merchants
- Cold start for new users: without existing spend history, the model will have to compensate
by using proxy factors such as age, home address, income, credit score, etc.
- Our model uses Yelp rating, # of reviews, whether a DCI privilege exists, and categorical
spend history to produce a score that indicates whether the user would prefer any given merchant.
- Higher score means higher predicted user preference
- The model then sorts the merchants by score (descending) to deliver a curated list of merchants.
- By providing a curated list of merchants, DCI will improve User-experience and drive usage of the app.
### Backend
- AWS Lamda
- AWS DynamoDB
- AWS API Gateway
### Machine-Learning and Data Libraries
- NumPy
- SciPy
- Pickle
### Frontend
- TamperMonkey