Books & Videos

Table of Contents

Chapter: Building Personalized Recommendation Engines

The Course Overview

03m 2s

Personalized and Content-Based Recommender System

10m 21s

Content-Based Recommendation Using Python

08m 15s

Context-Aware Recommender Systems

02m 23s

Creating Context Profile

04m 12s

Chapter: Building Real-Time Recommendation Engines with Spark

About Spark 2.0

03m 43s

Spark Core

03m 32s

Setting Up Spark

05m 11s

Collaborative Filtering Using Alternating Least Square

03m 34s

Model Based Recommender System Using pyspark

02m 18s

The Recommendation Engine Approach

09m 24s

Model Evaluation and Selection with Hyper Parameter Tuning

10m 25s

Chapter: Recommendation with Neo4j

Discerning Different Graph Databases

07m 7s


03m 23s

Building Your First Graph

04m 0s

Neo4j Windows Installation

01m 6s

Installing Neo4j on the Linux Platform

01m 48s

Building Recommendation Engines

03m 4s

Generating Recommendations Using Neo4j

01m 51s

Collaborative filtering Using the Euclidean Distance

03m 38s

Collaborative Filtering Using Cosine Similarity

02m 20s

Chapter: Building Scalable Recommendation Engines with Mahout

Setting up Mahout with General Introduction

04m 21s

Core Building Blocks of Mahout

10m 16s

Item-Based Collaborative Filtering

02m 50s

Evaluating Collaborative Filtering with User-Item Based Recommenders

03m 41s

SVD Recommenders

01m 55s

Chapter: The Future of Recommendation Engines

Future and Phases of Recommendation Engines

07m 52s

Using Cases to Look Out for

01m 57s

Popular Methodologies

04m 46s