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Posted on August 4, 2015 at 11:15am 0 Comments 1 Like
This post discusses building a recommendation model from movie ratings using an iterative algorithm and parallel processing with Apache Spark MLlib.
https://dzone.com/links/parallel-and-iterative-processing-for-machine-lear.html
Posted on April 13, 2015 at 9:14am 1 Comment 0 Likes
Recommendation engines help narrow your choices to those that best meet your particular needs. In this post, we’re going to take a closer look at how all the different components of a recommendation engine work together. We’re going to use collaborative filtering on movie ratings data to recommend movies. The key components are a collaborative filtering algorithm in Apache Mahout to build and train a machine learning model,…
ContinuePosted on March 30, 2009 at 10:30am 0 Comments 0 Likes
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Created by Michael Levin Dec 18, 2008 at 6:56pm. Last updated by Michael Levin May 4, 2018.
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Uber updates its Uber Eats Home Feed recommendation system using near real-time user sequence features and a Generative Recommender model. The system evolves from hand-crafted features to transformer-based sequence modeling, reduces feature freshness from 24 hours to seconds, and shifts from pointwise scoring to listwise GenRec for improved contextual ranking and real-time personalization.
By Leela Kumili
InfoQ has launched a five-week online AI Engineering certification for senior practitioners working on production AI systems, covering RAG, agents, AI platforms, evals, reliability, and operational trade-offs.
By Artenisa Chatziou
Discord has detailed how it rebuilt its database operations around a new internal orchestration framework called the Scylla Control Plane (SCP), enabling its small infrastructure team to automate large-scale ScyllaDB cluster management tasks that previously took days of manual work.
By Craig Risi
xAI has released Grok Skills together with enhancements to the Responses API for Grok 4.3, enabling persistent custom expertise that the model retains across all conversations.
By Daniel Dominguez
Cloudflare rebuilt Browser Run on its own Containers platform, delivering 4x higher concurrency and 50% faster response times. The upgrade completes a six-layer agent infrastructure stack: compute (Dynamic Workers + Sandboxes), orchestration (Dynamic Workflows), memory (Agent Memory), browsing (Browser Run), and commerce (Stripe Projects).
By Steef-Jan Wiggers
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