Beyond Collaborative Filtering

I wrote a couple of posts about some of the work on recommendation systems and collaborative filtering that we're doing at my job as a Data Scientist at Rubikloud:

• Beyond Collaborative Filtering (Part 1)

• Beyond Collaborative Filtering (Part 2)

Here's a blurb:

Here at Rubikloud, a big focus of our data science team is empowering retailers in delivering personalized one-to-one communications with their customers. A big aspect of personalization is recommending products and services that are tailored to a customer’s wants and needs. Naturally, recommendation systems are an active research area in machine learning with practical large scale deployments from companies such as Netflix and Spotify. In Part 1 of this series, I’ll describe the unique challenges that we have faced in building a retail specific product recommendation system and outline one of the main components of our recommendation system: a collaborative filtering algorithm. In Part 2, I’ll follow up with several useful applications of collaborative filtering and end by highlighting some of its limitations.

Hope you like it!