In 2020, the Machine Learning team at Hootsuite partnered with the Amplify team to create the Post Recommendation feature: a system that recommends social media posts to share based on what content a user has shared in the past. The goal was to increase the amount of content shared through Amplify and make it easier to find engaging content for our users. This post outlines the techniques used and the system architecture for this feature. As well, the performance in production will be discussed. Below in Figure 1 is a screenshot of the front facing design of the recommendation section.

Figure 1: Suggested Post UI Design

Background


The Machine Learning team at Hootsuite released the Suggested Reply to a small number of our customers (~200) on April 8th, 2020. The following details our design process for the system, as well as the final system architecture and preliminary usage metrics.

The Suggested Reply feature is used in Inbox in Hootsuite. When our customers receive messages from a social network they can respond to them in Inbox. Often, they use the same responses for many messages, and a major pain point that they had was storing and accessing these responses. Customers would use an external tool like Google Docs…


Hootsuite’s Premier Machine Learning Feature

In 2018 Hootsuite took steps to increase our machine learning capabilities for our product by forming a team to deliver the Suggested Tag Service. In addition to this feature, our team, along with teams from New York and Bucharest, was tasked with creating a GDPR compliant data lake for machine learning data, and a deployment pipeline template for future ML services. The following is an account of the final system design for this project.

Motivation

Hootsuite, as part of its offering, amalgamates all the messages directed through social channels such as Twitter, Facebook and Instagram into one platform. All manners…

Tyler Lanigan

Senior Developer working on machine learning at Hootsuite.

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