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Building Practical Recommendation Engines – Part 2
Building Personalized Recommendation Engines
The Course Overview
Personalized and Content-Based Recommender System
Content-Based Recommendation Using Python
Context-Aware Recommender Systems
Creating Context Profile
Building Real-Time Recommendation Engines with Spark
About Spark 2.0
Spark Core
Setting Up Spark
Collaborative Filtering Using Alternating Least Square
Model Based Recommender System Using pyspark
The Recommendation Engine Approach
Model Evaluation and Selection with Hyper Parameter Tuning
Recommendation with Neo4j
Discerning Different Graph Databases
Neo4j
Building Your First Graph
Neo4j Windows Installation
Installing Neo4j on the Linux Platform
Building Recommendation Engines
Generating Recommendations Using Neo4j
Collaborative filtering Using the Euclidean Distance
Collaborative Filtering Using Cosine Similarity
Building Scalable Recommendation Engines with Mahout
Setting up Mahout with General Introduction
Core Building Blocks of Mahout
Item-Based Collaborative Filtering
Evaluating Collaborative Filtering with User-Item Based Recommenders
SVD Recommenders
The Future of Recommendation Engines
Future and Phases of Recommendation Engines
Using Cases to Look Out for
Popular Methodologies
About Spark 2.0
Learn the basics of spark and its architectures.
Integrating spark data bricks
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