How we built recommendations, cut the vendor, and made money
summary
Describes building an in-house recommendation system that replaced a vendor off-the-shelf solution and generated additional revenue. Covers practical approaches to designing recommendations that work for all customer types while keeping ML infrastructure costs low, including migration away from the contractor solution.
AI-written summary · about
chapters (7)
- 0:00Introduction
- 4:46Vendor rejection reasons
- 8:21First success with product replacement
- 9:26Complementary recommendations
- 16:33Recommendation personalization
- 24:46Summary and global takeaways
- 36:38Q&A
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