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Exercises

  1. Tanimoto score. Find out what a Tanimoto similarity score is. In what cases could this be used as the similarity metric instead of Euclidean distance or Pearson coefficient? Create a new similarity function using the Tanimoto score.

  2. Tag similarity. Using the del.icio.us API, create a dataset of tags and items. Use this to calculate similarity between tags and see if you can find any that are almost identical. Find some items that could have been tagged “programming” but were not.

  3. User-based efficiency. The user-based filtering algorithm is inefficient because it compares a user to all other users every time a recommendation is needed. Write a function to precompute user similarities, and alter the recommendation code to use only the top five other users to get recommendations.


  

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