GPT3 Empowered Recommendation System
A low-code no ML setup!
Recommendation systems are so successful in many products and services we interact with every day. Like 40% of app installs on Google Play and 60% of watch time on YouTube comes from recommendations. Not even to mention the well-known TikTok’s recommendation system.
Traditionally, to build a ML based recommendation system, several components and stages are needed, like candidates generation, scoring, ranking. In terms of candidates generation, common approaches like content based filtering, collaborative filtering and Deep Neural Network (DNN) are well covered in many materials.
Now, as part of GPT3 exploration, here we demonstrate a low-code, no ML approach (yes, no model training and deploying) to build a movie recommender to help user find the related or similar movies given a specific movie or tv show title as the input.
Demo time: 👇
What are the components behind the scene?
- Shots 🥃 (examples/prompt, input for predictions)
