I am not a huge fan of machine learning or natural text processing (NLP) but I always have ideas in mind which require them. The idea that I will explore during this post is the ability to build a real time job search engine using twitter data. Tweets will contain the name of the company which if offering a job, the location of the job, and name of the contact person at the company. This requires us to parse the tweet for Person, Location, and Organisation. This type of problem falls under Named Entity Recognition. Read full blog here https://www.openshift.com/blogs/day-14-stanford-ner-how-to-setup-your-own-name-entity-and-recognition-server-in-the-cloud
Today is the fourth day of my challenge to learn 30 technologies in 30 days. So far I am enjoying it and getting good response from fellow developers. I am more than motivated to do it for full 30 days. In this blog, I will cover how we can very easily build blog recommendation engine using PredictionIO. I did not find much documentation around using PredictionIO with Java. So, this blog might help people looking for end-to-end PredictionIO Java tutorial. The full blog series can be tracked on this page.
What is PredictionIO?
PredictionIO is an open source machine learning server application written in Scala. It provides an easy to use REST API to build recommendation engines. It also provides client SDKs, which wraps the REST API. The Client SDKs are available in Java, Python, Ruby, and PHP programming languages. PredictionIO core is using Apache Mahout. Apache Mahout is a scalable machine learning library which provides various clustering, classification, filtering algorithms. Apache Mahout can run these algorithms on distributed Hapoop cluster. Continue reading “Day 4 : PredictionIO : How to Build A Blog Recommender”