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Building a food recommendation engine with Spark / MLlib and Play | Chimpler

Recommendation engines have become very popular in the last decade with the explosion of e-commerce, on demand music and movie services, dating sites, local reviews, news aggregation and advertising (behavioral targeting, intent targeting, ...). Depending on your past actions (e.g., purchases, reviews left, pages visited, ...) or your interests (e.g., Facebook likes, Twitter follows), the?
How to build your own Twitter Sentiment Analysis Tool | Datumbox

In this article we will show how you can build a simple Sentiment Analysis tool which classifies tweets as positive, negative or neutral by using the Twitter
10 Tips for Sentiment Analysis projects | Datumbox

In my Thesis project for the MSc in Statistics I focused on the problem of Sentiment Analysis. The Sentiment Analysis is an application of Natural Language
Anomaly DetectionThere has been an explosion of interest in Apache Spark as a new, alternative computing paradigm for Hadoop. It offers something to interest data scientists of all stripes, from interactive REPL to distributed functional programming to implementations of standard machine learning techniques.In fact, it promises big scalability improvements over MapReduce for iterative algorithms, like k-means clustering, which can be used to detect anomalous data in a huge data set, for example.This session will walk through a complete example of anomaly detection using Apache Spark and its MLlib subproject, as applied to the well-known network intrusion detection data set from KDD Cup 99. It will impart a taste of Scala (Sparks native language), Sparks core concepts like RDDs, and usage of MLlib for k-means clustering, in real-time on a Hadoop cluster. It will also introduce the concept of k-means clustering and how a data scientist would iteratively improve clustering in a session with Spark.Sean is Director of Data Science at Cloudera, based in London. Before Cloudera, he founded Myrrix Ltd, a company commercializing large-scale real-time recommender systems on Apache Hadoop. He has been a primary committer and VP for Apache Mahout, and co-author of Mahout in Action. Previously, Sean was a senior engineer at Google. He holds an MBA from the London Business School and a BA in Computer Science from Harvard. Sorry about the audio quality.http://datasciencelondon.org
Anomaly Detection with Apache Spark - Sean Owen
Data Science London Meetup
Unifying the LinkedIn Search Experience | LinkedIn Engineering

Unifying the LinkedIn Search Experience
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