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Building Recommendation Engines by Suresh Kumar Gorakala

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Content-based recommender systems

Building collaborative filtering is relatively easy. In the fifth chapter, we learned about building collaborative filtering recommender systems. While building those systems, we just considered the ratings given to a product and the information about whether a product is liked or not. With this minimal information, we built the systems. To many people's surprise, these systems performed very well. But these systems had their own limitations, such as the cold start problem explained in the previous chapters.

Assume a case of a user, Nick, giving five-star rating to a movie, say Titanic. What could have made Nick give that rating? May be the story of the film, the actors in the movie, the background score, or the ...

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