At Taboola, we work daily on improving our Deep-Learning-based content-recommendation model. We use it to suggest personalized news articles and ads to hundreds of millions users a day, so naturally we must stick to state-of-the-art deep learning modeling methods. But our job doesn’t end there – analyzing our results is a must too, and then we sometimes return to our data science roots and apply some very basic techniques. Let’s lay such a problem out. We are investigating a deep model that behaves rather strangely: it wins over our default model for what looks like a random group of advertisers, and loses for another group. This behavior is stable in the day to day, so it looks like there might be some inherent advertisers qualities (what we’ll call – campaign features) to blame for this. You can see a typical model behavior for 4 campaigns below. So we hypothesize that […]
At Taboola, our goal is to predict whether users will click on the ads we present to them. Our models use all kinds of features, yet the most interesting ones tend to be related to the users’ history. Understanding how to use these features well can have a huge impact on the model’s personalization capabilities, due to the user-specific knowledge they hold. User history features vary strongly between different users; for example, one popular feature is user categories – the topics a user had previously read. An example for such a list might look like this – {“sports”, “business”, “news”}. Each value in these lists is categorical and they have multiple entries, so we name them Multi-Categorical features. Multi-Categorical lists can have any number of values per user – which means our model must handle both very long lists and completely empty lists (for new users). Supplying inputs of unknown length […]
General This post describes how to use Android ContentProvider to allow automatic system initialisation for your library, therefore help make your library easier to integrate and control its flow. While this article mostly demonstrates one use case, you can use the idea in other cases as well. What is it all about? Always Strive To Simplify Integration Let’s assume you are writing code for a software library that would be used by an Android application. Most common flows require the app using your library to manually call the initialisation of your library and usually, provide it with their own Context. This will require your client to write a code along these lines: This article suggests using a Content Provider to allow: Completely autonomous initialisation, liberating you from having to ask the client for init at all. Avoiding the necessity of asking your client for their Application Context. […]
Knowledge sharing is critical for every company that wants to grow and improve. The bigger the company – the harder it gets. Inefficiency, a lack of alignment within your peers, difficulty training new workers – you name it. In this post we will take a look at the existing methods for knowledge sharing. How they can’t keep up with growth and fast paced changes, and why people are your best resource for knowledge. What is Knowledge? In general, there are three main types of knowledge that need to be shared in a software company – Technical Knowledge, Product Knowledge and Business Knowledge. When a new employee begins their job, most companies will help them to learn, using some of the more traditional methods to share knowledge: Learn from others – via frontal training or assigning a mentor Allow Self learning – online course, or from the company’s knowledge center (Atlassian, […]
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 […]
Hello Git user. In this blog post I will discuss a technique for a unique version calculation for every Git commit. You may ask why we need this, after all every commit in Git is identified by a unique sha1 hash. That’s right, let’s take 2 commits, 4bd92c9 and f5fc029, use their sha1 hash as a version and perform a simple A/B test. The test showed that 4bd92c9 is preferred to f5fc029. If this is the case, how can we tell: Which version is newer? If 4bd92c9 is included in f5fc029, or vice versa? What branch they were built from? It seems we need an alternative. The common standard for the versioning is a SemVer scheme. We will use its parts as follows: Major – manual increment Minor – every released feature will increment the minor Patch – will always be 0 Now let’s take a look at our […]