We made an open-source personalization engine

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Hey, HN! You probably know that the ordering of products on Amazon, posts in FB, and search results in Google is personalized for each visitor, as it directly affects conversion, click rate and engagement. But not everyone can afford to hire an army of PhDs to squeeze every penny out of the ranking, and not everyone agrees on the current (im)balance between privacy and profits. So we built Metarank, an open-source and privacy-focused personalization engine. It can rerank in real-time any type of content, using only the data you allow, and optimize metrics you define. We made a lot of proprietary DIY services for personalization in e-commerce in our past careers and heard so many complaints from other companies also struggling to implement personalization. It’s often considered "too risky" to spend 6+ months on an in-house moonshot project to reinvent the wheel without an experienced team and no existing open-source tools. Like other people in the industry, we were tired of building everything from the bottom up each time we approached personalization - it should be easy not only for Amazon to do such magical ML tricks, but for everyone else. A small demo of the tool with personalized recommendations: https://demo.metarank.ai A blog post on how this demo was made: https://medium.com/metarank/personalizing-recommendations-wi... The project itself: https://github.com/metarank/metarank

Visit product View on Hacker NewsMar 23, 2022

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Product HuntOn track for Day 1 leaderboard · Strong signals: google, using, open · Missing: mac, agents, macos

74%74% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, ide · Missing: https docs, excited, just released

71%71% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

62%62% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: personal, month, google · Missing: mobile apps, ios, entrepreneurs

46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: profit · Missing: arr, mrr, revenue

17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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