Collaborative Filtering Recommendation Engine

Within Collaborative Filtering, there are two main branches:

• Model-Based Collaborative Filtering
• Neighborhood Based Collaborative Filtering

This practices the implementation of Neighborhood Based Collaborative Filtering.

Similarity Metrics

In order to implement Neighborhood Based Collaborative Filtering, you were introduced to and applied a few techniques to assess how similar or distant two users were from one another:

• Pearson's correlation coefficient
• Spearman's correlation coefficient
• Kendall's Tau
• Euclidean Distance
• Manhattan Distance

Business Cases For Recommendations

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

• Relevance
• Novelty
• Serendipity
• Increased Diversity