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An Ensembled Latent Factor Model via Differential Evolution and Gradient Descent Optimization

 🧠Deep Learning  Content type: Academic
arxiv.org·

When your data model is the bottleneck: lessons from Medium’s feature store

 🎯Recommender Systems
thenewstack.io·

Nonequilibrium ion transport in a hybrid battery material

 🎯Recommender Systems
science.org·

How to Optimize Enterprise Knowledge Graphs for Scalable Digital Product Platforms

 🎯Recommender Systems
freecodecamp.org·

Object permanence

 🎯Recommender Systems  Content type: Blog
doctorow.medium.com
·

'Reading relationships, crunching stats'—184-times faster data analysis

 🎯Recommender Systems
techxplore.com·

Nestlé’s Stefan Kovačević on Building Brands for Both Humans and AI

 🎯Recommender Systems  Content type: Audio  Content type: News
adweek.com·

What Breaks When Multi-Agent Systems Scale

 🚀Model Deployment
digitalocean.com·

BRAND ANALYSIS: I IS FOR INSTAGRAM

 🎯Recommender Systems  Content type: Blog

Liftoff CEO on the IPO rebound, AppLovin comparisons, and why mobile apps remain an AI growth story

 🎯Recommender Systems  Content type: News
digiday.com·

Paramount is merging Pluto TV, BET+, and Paramount+ onto one tech stack to prepare for HBO Max

 🎯Recommender Systems  Content type: News
thenextweb.com·

Canada introduces bill to ban social media for children under 16

 🎯Recommender Systems  Content type: News
aljazeera.com·

I built a music discovery engine with 10M+ artists. 230 paying users in 2 months, zero moneys spent in marketing

 🎯Recommender Systems

Automate synthetic test coverage with Bits Testing

 🎯Recommender Systems  Content type: Blog
datadoghq.com·

Proteomics-constrained deconvolution reveals spatial cell-type programs in tumours

 🎯Recommender Systems  Content type: Academic
biorxiv.org·

When AI Enters the Therapy Room

 🤖AI  Content type: News  Content type: Blog

$\tau$-Rec: A Verifiable Benchmark for Agentic Recommender Systems

 🎯Recommender Systems  Content type: Academic
arxiv.org·

Shridipa/Elo-Learn: Elo Learn is an adaptive learning prototype built with FastAPI and Streamlit. The code is meant to show how student mastery, concept reasoning, recommendation logic and review scheduling can work together in one package.

 🎯Recommender Systems  Content type: Code
github.com··DEV

Pinterest and AWS Sign $4 Billion Deal to Power Visual Search

 👁️Attention Mechanisms
pymnts.com·

Shopee lays off staff globally, S’pore cuts reportedly hit product & engineering teams

 🔮ML
vulcanpost.com·

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