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 […]
Introduction Newsrooms are under constant pressure to deliver the most up to date, relevant, and engaging information possible. At Taboola, we are building tools to make this faster, easier, and now–predictable. As soon as an article is published the team has a critical eye on engagement data. Garnering insight on article performance as soon as possible is critical for guiding content strategy. Some articles receive wide attention immediately, drawing hundreds of thousands of page views within minutes, others may only see the first page view after a few hours. Taboola aims to narrow this gap even further by leveraging Machine Learning Models to predict article performance the moment after it becomes available to the reader. Read on for details on our latest research and fascinating discoveries around predicting article performance! Article Data Taboola Newsroom is a real-time optimization technology that empowers editorial teams with actionable data around what stories, headlines, […]
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 […]
For the past year, my team and I have been working on a personalized user experience in the Taboola feed. We used Multi-Task Learning (MTL) to predict multiple Key Performance Indicators (KPIs) on the same set of input features, and implemented a Deep Learning (DL) model in TensorFlow to do so. Back when we started, MTL seemed way more complicated to us than it does now, so I wanted to share some of the lessons learned. There are already quite a few posts about implementing MTL in a DL model (1, 2, 3). In this post I will share some specific points to consider when implementing MTL in a Neural Network (NN). I will also present simple TensorFlow solutions to overcome the discussed issues. Sharing is caring We wanted to start with the basic approach of hard parameter sharing. Hard sharing means we have a shared subnet, followed by […]
A joint post with Ofri Mann We went to ICLR to present our work on debugging ML models using uncertainty and attention. Between cocktail parties and jazz shows in the wonderful New Orleans (can we do all conferences in NOLA please?) we also saw a lot of interesting talks and posters. Below are our main takeaways from the conference. Main themes A good summary of the themes was in Ian Goodfellow’s talk, in which he said that until around 2013 the ML community was focused on making ML work. Now that it’s working on many different applications given enough data, the focus has shifted towards adding more capabilities to our models: we want them to comply to some fairness, accountability and transparency constraints, to be robust, use labels efficiently, adapt to different domains and so on. A slide on ML topics, from Ian Goodfellow’s talk We noticed a […]
Some of the problems we tackle using machine learning involve categorical features that represent real world objects, such as words, items and categories. So what happens when at inference time we get new object values that have never been seen before? How can we prepare ourselves in advance so we can still make sense out of the input? Unseen values, also called OOV (Out of Vocabulary) values, must be handled properly. Different algorithms have different methods to deal with OOV values. Different assumptions on the categorical features should be treated differently as well. In this post, I’ll focus on the case of deep learning applied to dynamic data, where new values appear all the time. I’ll use Taboola’s recommender system as an example. Some of the inputs the model gets at inference time contain unseen values – this is common in recommender systems. Examples include: Item id: each recommendable item gets […]
In the last couple of years deep learning (DL) has become a main enabler for applications in many domains such as vision, NLP, audio, click stream data etc. Recently researchers started to successfully apply deep learning methods to graph datasets in domains like social networks, recommender systems and biology, where data is inherently structured in a graphical way. So how do Graph Neural Networks work? Why do we need them? The Premise of Deep Learning In machine learning tasks involving graphical data, we usually want to describe each node in the graph in a way that allows us to feed it into some machine learning algorithm. Without DL, one would have to manually extract features, such as the number of neighbors a node has. But this is a laborious job. This is where DL shines. It automatically exploits the structure of the graph in order to extract features for […]
About a year ago we incorporated a new type of feature into one of our models used for recommending content items to our users. I’m talking about the thumbnail of the content item: Up until that point we used the item’s title and metadata features. The title is easier to work with compared to the thumbnail – machine learning wise. Our model has matured and it was time to add the thumbnail to the party. This decision was the first step towards a horrible bias introduced into our train-test split procedure. Let me unfold the story… Setting the scene From our experience it’s hard to incorporate multiple types of features into a unified model. So we decided to take baby steps, and add the thumbnail to a model that uses only one feature – the title. There’s one thing you need to take into account when working with these […]
So you just finished designing that great neural network architecture of yours. It has a blazing number of 300 fully connected layers interleaved with 200 convolutional layers with 20 channels each, where the result is fed as the seed of a glorious bidirectional stacked LSTM with a pinch of attention. After training you get an accuracy of 99.99%, and you’re ready to ship it to production. But then you realize the production constraints won’t allow you to run inference using this beast. You need the inference to be done in under 200 milliseconds. In other words, you need to chop off half of the layers, give up on using convolutions, and let’s not get started about the costly LSTM… If only you could make that amazing model faster! Sometimes you can Here at Taboola we did it. Well, not exactly… Let me explain. One of our models has to predict […]
Imagine you’re walking down the street and you see a nice car you’re thinking of buying. Just by pointing your phone camera, you can see relevant content about that car. How cool is that?! That was our team’s idea that awarded us first place in the recent Taboola R&D hackathon aptly named – Taboola Zoom! Every year Taboola holds a global R&D hackathon for its 350+ engineers aimed at creating ideas for cool potential products or just some fun experiments in general. This year, 33 teams worked for 36 hours to come up with ideas that are both awesome and helpful to Taboola. Some of the highlights included a tool that can accurately predict the users’ gender based on their browsing activity and an integration to social networks for Taboola Feed. Our team decided to create an AR (Augmented Reality) application that allows a user to get content recommendations, […]