Personalized software recommendations based on your stack
Personalized Product Recommendations AI answers the question "What software product should I use? A machine learning solution that can be used at scale to make software recommendations tailored to each user. Powering the models is data on nearly 33,000 products and over 375,000 companies that use and recommend them.
About Product Recommendations AI on Product Hunt
“Personalized software recommendations based on your stack”
Product Recommendations AI launched on Product Hunt on September 19th, 2017 and earned 318 upvotes and 29 comments, earning #2 Product of the Day. Personalized Product Recommendations AI answers the question "What software product should I use? A machine learning solution that can be used at scale to make software recommendations tailored to each user. Powering the models is data on nearly 33,000 products and over 375,000 companies that use and recommend them.
On the analytics side, Product Recommendations AI competes within Web App, Design Tools, Productivity, SaaS, Developer Tools, Artificial Intelligence and Tech — topics that collectively have 2.7M followers on Product Hunt. The dashboard above tracks how Product Recommendations AI performed against the three products that launched closest to it on the same day.
Who hunted Product Recommendations AI?
Product Recommendations AI was hunted by Kevin William David. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of Product Recommendations AI including community comment highlights and product details, visit the product overview.
Kevin William David