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Building Practical Recommendation Engines – Part 2
Building Personalized Recommendation Engines
The Course Overview (3:02)
Personalized and Content-Based Recommender System (10:21)
Content-Based Recommendation Using Python (8:15)
Context-Aware Recommender Systems (2:23)
Creating Context Profile (4:12)
Building Real-Time Recommendation Engines with Spark
About Spark 2.0 (3:43)
Spark Core (3:32)
Setting Up Spark (5:13)
Collaborative Filtering Using Alternating Least Square (3:34)
Model Based Recommender System Using pyspark (2:18)
The Recommendation Engine Approach (9:24)
Model Evaluation and Selection with Hyper Parameter Tuning (10:25)
Recommendation with Neo4j
Discerning Different Graph Databases (7:07)
Neo4j (3:23)
Building Your First Graph (4:00)
Neo4j Windows Installation (1:06)
Installing Neo4j on the Linux Platform (1:48)
Building Recommendation Engines (3:04)
Generating Recommendations Using Neo4j (1:51)
Collaborative filtering Using the Euclidean Distance (3:38)
Collaborative Filtering Using Cosine Similarity (2:20)
Building Scalable Recommendation Engines with Mahout
Setting up Mahout with General Introduction (4:21)
Core Building Blocks of Mahout (10:16)
Item-Based Collaborative Filtering (2:50)
Evaluating Collaborative Filtering with User-Item Based Recommenders (3:41)
SVD Recommenders (1:55)
The Future of Recommendation Engines
Future and Phases of Recommendation Engines (7:52)
Using Cases to Look Out for (1:58)
Popular Methodologies (4:46)
Setting up Mahout with General Introduction
Learn to setup the Apache mahout software.
Download the required Mahout jar
Create a Java Maven project in Eclipse
Set Java runtime as 1.7 or a higher version
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