In this video, I demonstrate how to build a content-based recommender system that provides personalized recommendations based on the user's watch history, specifically what they have liked and disliked.

✨ Follow along as I guide you through:
- Generating text embeddings
- Inserting collections into Qdrant Cloud
- Providing personalized recommendations based on user watch history, focusing on what they have liked and disliked
- Designing the app's frontend using Streamlit

🔥 Don't forget to 𝘀𝘂𝗯𝘀𝗰𝗿𝗶𝗯𝗲, 𝐬𝐦𝐚𝐬𝐡 the 𝗹𝗶𝗸𝗲 𝐛𝐮𝐭𝐭𝐨𝐧, and 𝐭𝐮𝐫𝐧 𝐨𝐧 the 𝐧𝐨𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐛𝐞𝐥𝐥 for more 𝗲𝘅𝗰𝗶𝘁𝗶𝗻𝗴 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀 and 𝘁𝘂𝘁𝗼𝗿𝗶𝗮𝗹𝘀.

🚀 Timestamps:
0:00 Introduction
0:21 Demo
0:49 Workflow - Flow diagram of Content-based Recommendation System
02:49 Setup Environment
03:17 Insert embeddings to Qdrant
12:42 Design Streamlit frontend
17:10 Generate Recommendations based on watch history and preferences
21:44 Test Recommendation System
22:24 Conclusion

Links:
💻 Code: https://github.com/Eduardovasquezn/mo...
☕️ Buy me a coffee... or an iced tea: https://www.buymeacoffee.com/eduardov
👔 LinkedIn:   / eduardo-vasquez-n  

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