Building a recommendation engine

To demonstrate both the content-based filtering and collaborative filtering approaches, we'll build a book-recommendation engine.

Book ratings dataset

In this chapter, we will work with book ratings dataset (Ziegler et al, 2005) 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 available at:

http://www2.informatik.uni-freiburg.de/~cziegler/BX/.

The Book-Crossing dataset comprises three files described at their website as follows:

  • BX-Users: This contains the users. Note that user IDs (User-ID) have been anonymized ...

Get Deep Learning: Practical Neural Networks with Java now with O’Reilly online learning.

O’Reilly members experience live online training, plus books, videos, and digital content from 200+ publishers.