Labs

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Building A Table Tennis Ranking Model

At Rubikloud, our wonderful Operations team regularly plans fun activities that the whole company can participate in such as movie nights, ceramic painting, and curling to name a few. However, my favourite activity by far is visible as soon as you get off the elevator. Championship match at our annual ping pong tournament Many of our Rubikrew are big fans of table...

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Data Science At Rubikloud

Over the last three years, Rubikloud has had some tremendous growth going from a team of less than a dozen to a fast-growing venture-backed startup with more than 80 people.  In this short time, we’ve assembled a team of talented engineers, retail experts and, of course, incredibly bright data scientists. With access to huge amounts of retail data spanning 10...

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What makes a good recommender system?

“I think you should move to Australia. You will be a lot happier there!”. How do you measure the quality of such a recommendation? In our tongue and cheek example, the basic approach would be to let a recommender system choose a large number of people, say 1,000, whom, from the recommender system’s perspective, will be happier in Australia. Then split...

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Gradient Boosting to the Xtreme – Part I

A key element of Rubikloud’s philosophy around software is that machine learning should be embedded into business software, not necessarily to replace human intuition, but rather, to augment and enhance it. The reasons for why we believe that to be true, you can refer to Kerry’s posts here: HOW TO USE MACHINE LEARNING TO FURTHER RETAIL ANALYTIC CAPACITY  AN INSIDER'S VIEW OF...

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Definition of Done

At Rubikloud, we focus heavily on shipping well-engineered products that are driven by data science and machine learning. That means we spend a lot of time prototyping and working through very large datasets and iterating over performance and feature considerations. This process requires many teams to work together to achieve a complete and shippable product. However, as any Product Manager knows,...

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Announcement: Data Science Seminars

At Rubikloud we're big fans of learning (and not just the machine kind)! Since our company revolves around all things data, we've organized a seminar series about just that: data science. Its purpose is to encourage anyone and everyone to learn more about data and how to use it. These seminars are open to the whole company and cover topics...

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Beyond Collaborative Filtering (Part 2)

Note: This is Part 2 of our series on recommendation systems and collaborative filtering. Please check out Part 1 of our series for the challenges of building a retail specific product recommendation system and an overview of collaborative filtering. Retail Product Recommendations Once a good collaborative filtering model has been built using matrix factorization, the individual dense latent customer and product vectors can...

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Beyond Collaborative Filtering (Part 1)

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...

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