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Building Recommendation Engines by Suresh Kumar Gorakala

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Summary

In this chapter, we have learned about popular recommendation engine techniques such as collaborative filtering, content-based recommendations, context aware systems, hybrid recommendations, and model-based recommendation systems, with their advantages and disadvantages. There are different similarity methods such as cosine similarity, Euclidean distance, and the Pearson coefficient. Subcategories within each of the recommendations are also explained.

In the next chapter, we learn about different data-mining techniques such as Neighborhood methods, machine learning methods used in recommendation engines, and their evaluation techniques such as RMSE and, Precision-Recall.

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