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Product Enrichment and Recommender Systems // Marc Lindner and Amr Mashlah // Coffee Sessions #114
Manage episode 337327656 series 3241972
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MLOps Coffee Sessions #114 with Marc Lindner, Co-Founder, COO, and Amr Mashlah, Head of Data Science of eezylife Inc., Product Enrichment and Recommender Systems, co-hosted by Skylar Payne.
// Abstract
The difficulties of making multi-modal recommender systems. How can it be easy to know something about a user but very hard to know the same thing about a product and vice versa? For example, you can clearly know that a user wants an intellectual movie, but it is hard to accurately classify a movie as intellectual and fully automated.
// Bio
Marc Lindner has a background in Knowledge Engineering. He's always extremely product-focused with anything to do with Machine Learning.
Marc built several products working together with companies such as Lithium Technologies, etc., and then co-founded eezy.
Amr Mashlah
Amr is the head of data science at eezy, where he leads the development of their recommender engine. Amr has a master's degree in AI and has been working with startups for 6 years now.
// MLOps Jobs board
MLOps Swag/Merch
https://mlops-community.myshopify.com/
// Related Links
Children of Time book by Adrian Tchaikovsky:
https://www.amazon.com/Children-Time-Adrian-Tchaikovsky/dp/0316452505
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Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Skylar on LinkedIn: https://www.linkedin.com/in/skylar-payne-766a1988/
Connect with Marc on LinkedIn: https://www.linkedin.com/in/marc-lindner-883a0883/
Connect with Amr on LinkedIn: https://www.linkedin.com/in/mashlah/
Timestamps:
[00:00] Takeaways
[04:20] Introduction to Marc Lindner and Amr Mashlah
[06:08] eezylife Inc.
[08:43] Integration with different tools
[10:13] Richer data for eezy
[12:58] Challenges with different providers
[15:25] Labeling solutions
[16:00] Maodal.com
[18:18] Handling multi-modal recommendation
[20:11] Figuring out the process of ingesting the data
[23:16] Ontology of sorts
[27:21] Talking in-depth about technical pieces
[28:40] Handling cold start
[31:42] Bad recommendations
[37:24] Curation vs hard-coded rules
[39:53] Social features
[41:57] eezy's vision
[45:12] Suggested recommendations
[46:48] Preferences from inferred interactions
[51:45] Ethical considerations
[54:19] Wrap up
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