03. Text: What's Ahead?

### What's Ahead

Types of Recommendations

In this lesson, you will be working with the MovieTweetings data to apply each of the three methods of recommendations:

  1. Knowledge Based Recommendations
  2. Collaborative Filtering Based Recommendations
  3. Content Based Recommendations

Within Collaborative Filtering, there are two main branches:

  1. Model Based Collaborative Filtering
  2. Neighborhood Based Collaborative Filtering

In this lesson, you will implement Neighborhood Based Collaborative Filtering. In the next lesson, you will implement Model Based Collaborative Filtering.

Similarity Metrics

In order to implement Neighborhood Based Collaborative Filtering, you will learn about some common ways to measure the similarity between two users (or two items) including:

  1. Pearson's correlation coefficient
  2. Spearman's correlation coefficient
  3. Kendall's Tau
  4. Euclidean Distance
  5. Manhattan Distance

You will learn why sometimes one metric works better than another by looking at a specific situation where one metric provides more information than another.

Business Cases For Recommendations

Finally, you will look at the four ideas needed for businesses to implement successful recommendations to drive revenue, which include:

  1. Relevance
  2. Novelty
  3. Serendipity
  4. Increased Diversity

At the end of this lesson, you will have gained a ton of skills to build upon or to start creating your own recommendations in practice.

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