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:

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!

I'm Brian Keng, a former academic, current data scientist and engineer. This is the place where I write about all things technical.

Twitter: @bjlkeng


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