Recommending future franchise locations

I grew up in Margao, Goa – a place known for beaches, greenery and a happy-go-lucky lifestyle. As kids, my friends and I loved junk food. Every summer we’d talk about how a friend of mine went to an aunt’s house in Mumbai or some other big city and how he’d eaten at Domino’s pizza or KFC while he was there. We’d always ask the same questions: “How was the food?” and “Is it better than pizzas here?”, and we’d always get the same answer: wide eyes and a hum of approval. More than that even, I think we liked the idea of being part of a big city. Being big time. Eating pizza at Domino’s was definitely worth bragging rights as a middle schooler in Goa.

Margao was a sleepy town. There were no Domino’s or McDonald’s or Inox movie halls. On some weekends, my friends and I would take an hour long bus to Panaji just to get to eat at Domino’s pizza and watch a movie at Inox. We would plan this trip for weeks and spend a lot of that time wishing that Domino’s would someday open in Margao. This got me thinking about ways by which I’d know when and where a new franchise would open. I needed to know when Domino’s was coming to town.

Bogmalo beach, Margao

A map of Margao

Now, 5 years later, I finally have the skills to try and do this. The next few blog posts are going to be a slight bit different; I’m going to be exploring the possibility of future franchises using recommender systems and R. I found a course offered by the University of Minnesota and this link quite helpful in understanding how recommender systems work. There’s plenty of recommender algorithms out there. I studied recommender systems and found that User-based collaborative filtering would best serve my purpose of, well, predicting the future.

User-based collaborative filtering basically means this: If you like pizza and I like pizza, we have similar tastes. If the both of us also like Thai food, we’re more similar. This means that if you like Chinese food, I’ll probably like it too. The more foods we like in common, the more likely it is that we have similar tastes. User-based collaborative filtering does exactly this: it predicts what I’ll like based on what similar users (you) have liked. This system is used by Amazon, Facebook, Twitter and many other heavyweights in the tech world.

Instead of using recommender systems for ratings and ‘you may also like’ products, I decided to use it to map companies and their store locations. To recommend to companies where they should open their next store. Stay tuned for what’s to come.