Recommendation systems are a big part of Online applications because they are related to understanding people. Few well-known implementations that we come across daily are while buying things on Amazon, watching movies on Netflix, browsing through photos on Instagram / Pinterest. This session plans to demystify Content Recommendations putting it in the context of Natural Language Processing (NLP) and Machine Learning, covering their implementations spanning use and complexity levels - with Similarity implementations and Collaborative Filtering to Matrix Factorization methods. We will deep dive into the different ways of coming up with the Recommendations, what suits a specific need and the Pros & Cons.
The session will also explain how to implement the Engine using an Unsupervised Machine Learning approach, integration with Drupal, and touch upon the Architectures. Few use cases across domain and complexities will be highlighted.
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