Engineering: Personalization & AI

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…

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 p…

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 …

Personalized Job Feeds and Machine Learning

Product managers for job sites face two fundamental problems. First, the top candidates are not actively looking for a job, making them difficult to seek out and find. Second, the top jobs are quickly filled and typically attract in-network candidates. So, while you have top cand…

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 t…

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 th…

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 engagemen…

Activity Stream Personalization

Personalization comes in many flavors, and the Data Science team at Stream helps you build your own personalization engine based on your specific needs. In conjunction with our Analytics client, we can use both engagement and feed data to power and improve your app’s experience u…

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, some…

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: Sho…

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 co…

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…

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 engin…