Researchers introduced Mender, a recommendation system that uses Llama 3 to infer customer preferences from text like product reviews and descriptions.
Instead of relying directly on raw text, which can contain noise, Mender extracts explicit preferences, such as a liking for bold colors, and uses them to predict future purchases. Tested on Amazon and Steam datasets, it outperformed traditional systems like TIGER, especially in categories with highly variable customer tastes.
Read our summary of the paper in The Batch: https://t.co/XsXtQnv4CT
I recently participated in a 24-hour national-level hackathon, where I built Smart Scanner, a Web3 project aimed at enhancing blockchain security! 🚀
💡 What is Smart Scanner?
🔹 A Web3-powered tool for detecting risks in blockchain transactions
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🚀 Excited to announce that I'll be participating in the upcoming Hackathon at Jagannath University on March 29-30!This is a great opportunity to collaborate, innovate, and push the boundaries of technology with like-minded enthusiasts
Understanding Hash Algorithms in Blockchain! 🔍
Join me as I dive into the fascinating world of hash functions and how they secure our digital transactions. 🚀
From input to unique hash output, see it all in action!
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