The art of feed curating: Our approach to generating personalized feeds that match users' interests

September 5, 2023

Feeds are an essential part of every social network. The same applies here at Hashnode. Until now, we have used a very basic and generic algorithm to generate feeds mainly based on hot ranking algorithms. Over time, we noticed that users struggle to find the content they are genuinely interested in on our platform. This is why we have decided to power up our feed game with personalized feeds 🎉

Why are personalized feeds such a big deal? 🤔

When you visit a community platform, you want to see content that speaks to you. That's where personalized feeds come in! Instead of a generic feed, personalized feeds focus on what the user finds interesting and valuable. They do this by looking at your past interactions, interests, and other factors to serve up content that's right up your alley 🎯

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Personalized feeds at Hashnode focus on what YOU find interesting!

As a result, users get an enjoyable and engaging experience. Personalized feeds help to keep users happy and active by catering to their preferences and interests.

Let's explore how we implemented personalized feeds at Hashnode 🚀

Personalized feeds for different users – let's see the result

Let's examine the concept of personalization. From a user's standpoint, assuming that the feed will differ for each individual is reasonable. When we query our feed endpoint for two distinct users, we obtain the following results:

The users follow different tags and engage with Hashnode in unique ways. The outcome? A feed that is tailored to each user.
If you want to see this in action, visit our Homepage.

Let's see how we have built this experience from an engineering perspective 👀

How to generate personalized feeds without machine learning?

For most platforms, machine learning is the go-to approach to calculate and personalize stuff. These techniques and algorithms have existed for a long time and are rooted in the early e-commerce systems to recommend other stuff that you may find interesting.
Platforms like Facebook, Twitter, and Instagram have shown machine learning models can be effective when trained with enough and correct data.

![a robot sitting on top of a wooden bench](https://images.unsplash.com/photo-1620712943543-bcc4688e7485?ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D&auto=format&fit=crop&w=1000&q=80 align="left")

At Hashnode, we decided initially not to go the machine learning route for our personalized feeds. Although incorporating machine learning into our platform is a long-term objective, we have opted for an alternative approach in the interim.

Starting with an ML approach is challenging. You need to have some knowledge about creating machine learning models and pipelines. You need to integrate ML into your existing platform. Multiple options are available: either go with self-hosting and self-creating/training your models or use a service where you can train models by feeding them data. The one point that stuck out was that you must understand your data and what you want to achieve with the model.

We decided to go with a ranking-based approach, as we wanted to verify assumptions we already had about our content and get out a better algorithm as fast as possible. To generate personalized feeds without machine learning, we have developed our own unique recipe for feed generation. This method considers various aspects and user behavior patterns that we believe will be most beneficial in ranking posts for each user. By carefully considering multiple factors, we can curate a feed that caters to our users' specific interests and needs.

Going with this route gives us the leverage to understand the influence different weights and data points have on the quality of our feed. It enables us to provide a more customized experience for our community members and continually refine and improve our feed generation process. Resulting in a faster delivery of even more relevant and captivating content as we gather more data and insights on user preferences and behavior.

In conclusion, while machine learning and AI algorithms are powerful tools for generating personalized feeds, traditional techniques can still be employed to create a tailored content experience.

Let's look at what we use to develop a personalized feed for a user 🫣

Which data is relevant for feed calculation?

To effectively personalize feeds and generate accurate content rankings for each user, it is crucial to consider several pieces of data that can provide valuable insights into their preferences and behavior. These data points include:

User-specific:

Followed Tags: The tags a user follows indicate their topics of interest. Content with matching tags should be prioritized higher.
Following the Author/Blog: A user who follows an author or blog will likely enjoy that content source. Content from followed authors/blogs should be weighted more heavily.
Reading History: What articles a user has read in the past shows their preferred content types and subjects. Similar new content should be ranked higher.

Community-specific:

Likes: If an article has received many likes from the community, it is likely high quality and relevant to some users.
Comments: More engagement in comments also indicates relevance and popularity.
Views: Higher-viewed content is generally more relevant to more users.
Featured: Featured articles are chosen for their relevance and quality.
Recency: Newer content is likely fresher and more timely. Furthermore, this boosts newly published content and freshens up the feed.

Blog-specific:

Pro Account: Pro on a blog can indicate that the authors use Hashnode on a more sophisticated level and leverage features such as Hashnode AI, which allows them to generate even more high-quality content.
Custom Domain: Custom domains indicate authors leveraging Hashnode to build their brand and publish high-quality content.

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The above list shows roughly the importance of the weights in a declining order. The main focus lies on the User-specific weights, whereas the following categories have less and less influence on the score.

Ranking posts to generate personalized feeds

Now, it's time to look at how weights are calculated and how they influence the score of a post within a user's feed. 🧑‍💻

The User-specific weights are rather straightforward. We can check if the user is following the Author/Blog, the following tags added to the post, and if the article is from an author in the reading history of the user:

Thanks for reading 👏

@codingfuchs

Freelance Fullstack Engineer | AWS Community Builder | Serverless & Frontend = ❤️