4 Content-based recommendations

 

This chapter covers

  • Designing proper graph models for a content-based recommendation engine
  • Importing existing (nongraph) datasets into the designed graph models
  • Implementing working content-based recommendation engines

Suppose you would like to build a movie recommender system for your local video rental store. Old-fashioned Blockbuster-style rental shops have largely been put out of business by new streaming platforms such as Netflix (http://mng.bz/0rBx), but some still exist here and there. There’s one in my town. Back when I was at university (a long time ago), I used to go there with my brother every Sunday to rent some action movies. (Keep this preference in mind; it will be useful later!) The important fact here is that this scenario inherently has a lot in common with more-complex online recommender systems, including the following:

4.1 Representing item features

4.2 User modeling

4.3 Providing recommendations

4.4 Advantages of the graph approach

Summary

References