AI-Powered Content Tagging & Recommendation System

Problem:

A client managing a large library of user-generated content needed an automated solution for accurately tagging and recommending videos. Manual tagging was inconsistent and inefficient, which reduced discoverability and user engagement.

Solution:

I designed and developed a machine learning system using zero-shot classification to automate content tagging and similarity-based recommendations. The model processes descriptions, transcripts, and metadata to assign relevant tags and recommend similar content. I deployed the solution with RESTful APIs and integrated human review checkpoints for quality control.

Expected Impact:

The system reduced manual tagging workload by over 80% and improved content discovery efficiency. Stakeholder testing showed better user engagement through higher video click-through rates and session durations.

Steps Taken:

Collected training data and analyzed user tagging patterns.
Fine-tuned NLP models for content classification and similarity detection.
Built a secure API layer to connect the model to the client’s content system.
Implemented validation tools for human-in-the-loop moderation.