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11 November 2019

Our core business at Taboola is to provide the surfers-of-the-web with personalized content recommendations wherever they might surf. We do so using state of the art Deep Learning methods, which learn what to display to each user from our growing pool of articles and advertisements. But as we challenge ourselves manifesting better models and better predictions, we also find ourselves constantly facing another issue – how do we not listen to our models. Or in other words: how do we explore better? As I’ve just mentioned, our pool of articles is growing, meaning more and more items are added each minute – and from an AI perspective, this is a major issue we must tackle, because by the time we finish training a new model and push it to production, it will already have to deal with items that never existed in its training data. In a previous post, I’ve […]

6 June 2019

If you happen to write code for a living, there’s a pretty good chance you’ve found yourself explaining another interviewer again how to reverse a linked list or how to tell if a string contains only digits. Usually, the necessity of this B.Sc. material ends once a contract is signed, as most of these low-level questions are dealt with for us under-the-hood of modern coding languages and external libraries. Still, not long ago we found ourselves facing one such question in real-life: find an efficient algorithm for real-time weighted sampling. As naive as it might seem at first sight, we’d like to show you why it’s actually not – and then walk you through how we solved it, just in case you’ll run into something similar. So buckle up, we’ve got some statistics and integrals coming up next! Why We Need Weighted Sampling in Production? At Taboola, our core business is to personalize […]

13 November 2018

If you’ve been following our tech blog lately, you might have noticed we’re using a special type of neural networks called Mixture Density Network (MDN). MDNs do not only predict the expected value of a target, but also the underlying probability distribution. This blogpost will focus on how to implement such a model using Tensorflow, from the ground up, including explanations, diagrams and a Jupyter notebook with the entire source code. What are MDNs and why are they useful? Real life data is noisy. While quite irritating, that noise is meaningful, as it gives a wider perspective of the origins of the data. The target value can have different levels of noise depending on the input, and this can have a major impact on our understanding of the data. This is better explained with an example. Assume the following quadratic function: Given x as input, we have a deterministic output […]

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