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Building Recommendation Engines by Suresh Kumar Gorakala

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Building recommendation engines

In this section, we will learn how to generate collaborative filtering recommendations using three approaches. They are as follows:

  • A simple count of co-rated movies
  • Euclidean distance
  • Cosine similarity

I would like to highlight a point at this junction. In earlier chapters, we learnt that for building recommendation engines using heuristic approaches, we used similarity calculations such as Euclidean distance/cosine distance. It is not necessary to use only these approaches; we are free to choose our own way of computing the closeness or extracting the similarity between two users just by simple counts as well, for example, similarity between two users can be extracted just by counting the number of the same movies ...

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