Recommendation systems have become an integral part of our daily lives, shaping our experiences on e-commerce platforms, content streaming services, and social media networks. They help users navigate large catalogs, find relevant items, and discover new products or content they might like. But how do we know if a recommendation system is doing a good job? This tutorial will guide you through the essential metrics for evaluating the performance of your recommendation system so that it effectively meets user needs and improves their experience.

Timecodes
0:00 – Introduction
0:38 – Group 1. Accuracy Metric for Recommender System
2:59 – Group 2. Ranking Metrics for Recommender System
11:19 – Group 3. Coverage and diversity metrics for recommender system

️ Blog post with code for this video: https://medium.com/@AmyGrabNGoInfo/the-ultimate-guide-to-evaluating-your-recommendation-system-d4fc8d4423cc
Code notebook: https://mailchi.mp/05ebf8b5fa31/uiv89ygm8v
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️ Playlist recommendation system: https://www.youtube.com/playlist?list=PLVppujud2yJqshyM80nNDZgye-AFufyqF
Videos mentioned in the video
Recommendation System: User-based Collaborative Filtering: https://medium.com/p/recommendation-system-user-based-collaborative-filtering-a2e76e3e15c4
Recommendation System: Item-based Collaborative Filtering: https://medium.com/grabngoinfo/recommendation-system-item-based-collaborative-filtering-f5078504996a

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