Recommendation Engine: Figuring Out What Your Friends Like (Bill Schreiber)

Recommendation Engine: Figuring Out What Your Friends Like

A recommendation engine is software that predicts what a user may or may not like based on previous expressed likes or dislikes. It can be used as an alternative or in conjunction with searches since it helps users discover products or content that they may not have otherwise come across. Recommendation engines are a big part of Amazon, Facebook, movie and many, many content sites across the internet.

As humans, we recommend content, products or movie for friends or family regularly. “Hey, I saw a movie last night, I know you will like!” For a computer, this is a hard thing to do.

In my talk, I will talk about how to define “similarity” then how to relate that similarity to make recommendations. We will talk about several approaches to define “similarity” and how this might be approached in a production environment to develop a recommendation engine. I will also talk about ways to verify recommendations and the efficacy of your approach or data.

The concept of “similarity” between things is also a useful concept in development. If two things are similar, one may infer characteristics that have nothing to do with the correlational evidence given it. For instance, a child holding a chinchilla means that the chinchilla is probably the child’s pet. Since it is a pet, one may infer that it can be approached and petted. The ability to infer means that a computer doesn’t have to be told everything it needs to know in its environment; it can make educated guesses.

Understanding what a recommendation engine or collaborative filter can do and how to establish “similarity” between items are useful tools in the software architect’s tool belt. These tools expand the services and capabilities of the software we write for the customers we serve.

Bio

My name is Bill Schreiber, and I am presently a principal developer at RSA in Bedford MA where my group develops, maintains and augments the systems that run the company in their IT department. I enjoy building robots as a hobby, kayaking and skiing. I am passionate about development, security and always looking to learn, grow and become a better person and engineer.

Venue and Food:

We meet at Magenic (see address above - it is in Suite #450) at 6-8 pm. As usual, there will be pizza and sodas provided. Please RSVP through this site if you will be attending.