Engineering : Personalization & AI

Most Recent Posts

How to Achieve a 9ms Inference Time for Transformer Models

Interested in Moderation for your product? Check out Stream's Auto-Moderation Platform! It is crucial for the technology platforms to moderate any harmful content as early as possible. Most modern moderation tools take a few hundred milliseconds to a few seconds to detect harmful content. Often the action against detected harm is taken after the harm
Read More5 Min Read

Transformations in Machine Learning

On 8th September 2020, an article in the Guardian was written by a robot called GPT-3. They asked the robot to write an article about why humans should not be scared of robots and Artificial Intelligence. The human editors wrote the introduction for the article and instructed GPT-3 to generate the next possible sentences iteratively.
Read More17 Min Read

Activity Feed Personalization 101: Top Feed Features to Improve User Engagement

Personalization comes in many flavors, and the data science team at Stream can help you build your own feeds personalization engine based on your specific needs. In conjunction with our analytics client we recommend tracking every event for every user, such as clicking on a link) we use both engagement and feed data to power
Read More4 Min Read

Google Feed Personalization and Recommender Systems

Lately, I’ve been using Google’s feed on Android and it contains several interesting best practices for content discovery. Google’s feed strikes an effective balance between machine learning and follow relationships. With the recent advancements in AI, it can be hard to know when to apply AI and when to use a more manual method. This
Read More4 Min Read

Building an End-to-End Deep Learning GitHub Discovery Feed

There's hardly a developer who doesn’t use GitHub. With all those stars, pulls, pushes and merges, GitHub has a plethora of data available describing the developer universe. As a Data Scientist at Stream, my job is to develop recommender systems for our clients so that they can provide a better user experience for their customers. With that said, I wanted to see if I could build a recommendation
Read More11 Min Read

Moving Beyond EdgeRank for Personalized Newsfeeds

This blog post is broken into two parts and harkens back to learnings from a prior post. The sum of all these parts is altogether my best effort to provide you with a framework of how to take the creation of personalized news feeds to the next level. Part 1: Theory behind a very basic
Read More6 Min Read

Building Your Own Instagram Discovery Engine: A Step-By-Step Tutorial

Isn’t it great how Instagram’s “Explore” section displays content that matches your interests? When you open the application, the content and recommendations shown are almost always relevant to your specific likes, interests, connections, etc. While it may be fun to think we’re the center of the Instagram universe, the reality is that personalized, relevant content
Read More7 Min Read

Follow Recommendations in Social Networks

Social media is a series of networks connecting individuals, companies, organizations, and groups to one another. These networks can transcend local, national, and international borders connecting people to networks far and wide. With all those connections, how can a user find the ones that they want to connect with? That’s where follow suggestions come in.
Read More4 Min Read

Best Practices for Recommendation Engines

In this blogpost I will describe how to implement a feature-rich activity feed that will make relevant and accurate personalization algorithms easier to implement. As we have already explored in previous blog posts, app personalization is linking activity feeds and user engagement data. In most cases, a well thought out feed structure provides valuable information
Read More3 Min Read

Factorization Machines for Recommendation Systems

As a Data Scientist that works on Feed Personalization, I find it it important to stay up to date with the current state of Machine Learning and its applications. Most of the time, using some of the better-known recommendation algorithms yields good initial results; however, sometimes a change in the model is essential to provide customers
Read More6 Min Read

Example Ranking Methods for Your Feeds

In this short tutorial we will show you how to use Custom Ranking for your activity streams and news feeds. By default all feeds on Stream are ranked chronologically. Custom ranking allows you to take full control over how your feeds are sorted. Some common use cases include: Showing popular activities higher in the feed
Read More4 Min Read

Personalization & Machine Learning for News Feeds and Social Networks

Winds is an open source RSS reader is powered by React, Redux, Sails and Stream. This tutorial explains how we’ve built personalization for Winds, as an example of how using Stream makes it easy to build personalized feeds. About Personalization Personalization is a very broad concept. In this case, personalization equates to leveraging engagement data
Read More5 Min Read

An Introduction to Contextual Bandits

In this post I discuss the Multi Armed Bandit problem and its applications to feed personalization. First, I will use a simple synthetic example to visualize arm selection in with bandit algorithms, I also evaluate the performance of some of the best known algorithms on a dataset for musical genre recommendations. What is a Multi-Armed Bandit? Imagine
Read More6 Min Read

Fast Recommendations for Activity Streams Using Vowpal Wabbit

The problem of content discovery and recommendation is very common in many machine learning applications: social networks, news aggregators and search engines are constantly updating and tweaking their algorithms to give individual users a unique experience. Personalization engines suggest relevant content with the objective of maximizing a specific metric. For example: a news website might want to increase
Read More5 Min Read