Building a personalized feed without training a recommender
📰 Medium · Machine Learning
Learn to build a personalized feed without training a recommender using vector similarity over embeddings
Action Steps
- Build a vector database to store user embeddings
- Calculate engagement scores for each user-item interaction
- Apply nearest-neighbour lookup to find similar items
- Implement an explore feature to suggest new items
- Configure a threshold for vector similarity to determine personalized feed items
Who Needs to Know This
Machine learning engineers and data scientists can benefit from this approach to create personalized feeds for users without requiring extensive training data or complex recommender systems
Key Insight
💡 Vector similarity over embeddings can be used to build personalized feeds without requiring extensive training data
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🚀 Build personalized feeds without training a recommender! Use vector similarity over embeddings 🤖
Key Takeaways
Learn to build a personalized feed without training a recommender using vector similarity over embeddings
Full Article
How I built a personalized feed using vector similarity over embeddings — the engagement score, the nearest-neighbour lookup, the explore… Continue reading on Write A Catalyst »
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