Developed by Dwight Bedsaul. Social Media platforms live and die by relevance.
If users open an app and instantly see content they care about, they stay engaged. If the feed feels random or repetitive, people leave fast. That challenge is what inspired me to build my own recommendation engine for ContentSocial or for any WordPress platform. The project, available on GitHub, was designed to experiment with personalized content feeds inside a WordPress social network environment. Instead of relying on a basic chronological feed, I wanted to create a smarter system that learns what users interact with and adjusts the experience over time.
Recommendation systems have become one of the core technologies behind modern platforms because personalization directly affects engagement and retention. Large scale recommendation systems are now used everywhere from video platforms to social media applications and e-commerce systems.
One thing I noticed while working with social platforms is that users all consume content differently. Some users primarily watch videos. Others engage heavily in groups, live streams, or photo posts. A default one size fits all feed usually doesn’t work very well once a community begins growing. I wanted to build a system that could priortize content based on engagement, learn from user behavior, surface more relevant posts, reduce feed clutter, and keep users active longer.
The goal was not to copy massive enterprise algorithms from companies with unlimited infrastructure. Instead, I focused on creating a lightweight recommendation engine that could realistically run in a self hosted WordPress environment.
The engine analyzes user interactions across the platforms and assigns weighted values to different activities. Each interaction helps build a profile of what the user appears to enjoy. Instead of simply showing the newest content first, the system scores posts based on revelance and engagement probability. This creates a more dynamic feed where active and meaningful content has a better chance of being surfaced. I also experimented with content filtering controls so users could influence their own feed preferences. This idea came from observing how modern recommendation systems increasingly combine algorithmic ranking with user adjustable preferences.
The project taught me a lot about the intersection between user psychology and backend engineering. Building a recommendation engine is not just about algorithms. It’s about understanding how people interact online. Small changes in feed ranking can completely change how a platform feels to users. Even simple weighting adjustments can noticeably affect engagement patterns.
Building this recommendation engine pushed me to thin beyone basic web development and focus more on platform behavior, engagement design, and scalable personalization. As I continue building projects involving social, networking, live streaming, AI assisted moderation, and personalized feeds I plan to keep refining these systems further.
Porjects like this remind me why I enjoy development in the first place. Solving real problems by creating better experiences for the people on the platform. You can view the project here:
https://github.com/eldorado101/Peepso-Recommendation-Engine
Other Links of interest;
https://www.linkedin.com/in/dwight-bedsaul-3b7a92344/
https://www.youtube.com/@dwightbedsaul
Written by Dwight Bedsaul
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