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Building Recommendation Engines by Suresh Kumar Gorakala

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Summary

In this chapter, we saw various data-mining steps that are popularly used in building recommendation engines. We started by learning similarity calculations, such as Euclidean distance measures, followed by mathematical models, such as matrix factorization techniques. Then we covered supervised and unsupervised machine learning techniques, such as regression, classification, clustering techniques, and dimensionality reduction techniques. In the last sections of the chapter, we covered how information retrieval methods from natural language processing, such as vector space models, can be used in recommendation engines. We concluded the chapter by covering popular evaluating metrics. Till now we have covered theoretical background required ...

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