•almost 4 years ago
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 using cutting edge Machine Learning algorithms. Here are some of the most common features that we have implemented in the past:
Discovery feeds usually contain activities that are not directly available through a user’s network but that might be of interest to them. A prominent example of a discovery feed is Instagram’s Explore section: Content on a discovery feed is typically based on:
- Trending content
- Activities that match your interests
- Content that is popular amongst your extended (friends of friends) network.
Your interest profile is based on the types of activities you engage with -- if you click many pictures of snowboarding on Instagram it will, over time, show you more content related to snowboarding. Over here at Stream we use our analytics package combined with the personalization offering to build a similar experience.
Personalized Feed Ranking
This is our most advanced feature; you can think of it as a much more powerful implementation of our Ranked Feeds. The ranking for personalized feeds is based on each user’s individual interest profile. It allows you to build very complex ranking rules that show the most relevant content at the top of everyone’s feed, such as “while you were away” content, content from users’ inner circle of friends, fresh activities or recommended items. One of the most impressive examples of a personalized feed is Quora. Quora adjusts their main feed and their emails based on the content you interact with. A few weeks ago I caught up with the Star Wars saga and clicked a link on Quora about the best order to watch the episodes. Quora immediately picked up on my new interest and started showing me various trivia about Star Wars. They combined this with content about Entrepreneurship, Scalability and Programming that they know I’m already interested in.
Email Optimization (Ecommerce and Social)
The detailed interest profile, based on user engagement, allows you to send the most relevant content and “stickiness” to keep users coming back to your application. This is also very effective for ecommerce sites with a community aspect.
If your application implements an activity feed, it is very likely that it will contain a social component. Most successful social apps have a highly connected network. The Personalization Engine at Stream can help the organic growth of your app by suggesting other feeds to follow (such as users or topics) based on authority, common interests and friend proximity.
Item recommendations are an essential feature if you need to provide users with the content that is most relevant for them. There are thousands of possible applications for item recommendations, for instance: recommending songs to users, matching jobseekers with open positions, and recommending fashion products that are similar to previous items they have bought.
The features offered by our Personalization service are continuously expanding. Feel free to reach out to us if you want to set up a trial for Personalization.