To demonstrate both the content-based filtering and collaborative filtering approaches, we'll build a book recommendation engine.
Building a recommendation engine
Book ratings dataset
In this chapter, we will work with a book ratings dataset (Ziegler et al., 2005) that was collected in a four-week crawl. It contains data on 278,858 members of the Book-Crossing website and 1,157,112 ratings, both implicit and explicit, referring to 271,379 distinct ISBNs. User data is anonymized, but with demographic information. The dataset is taken from Improving Recommendation Lists Through Topic Diversification, Cai-Nicolas Ziegler, Sean M. McNee, Joseph A. Konstan, Georg Lausen: Proceedings of the 14th International World Wide Web...
