Chapter 3. Representing recommender data
This chapter covers
- How Mahout represents recommender data
- DataModel implementations and usage
- Handling data without preference values
The quality of recommendations is largely determined by the quantity and quality of data. “Garbage in, garbage out,” has never been more true than here. Having high-quality data is a good thing, and generally, having lots of it is also good.
Recommender algorithms are data-intensive by nature; their computations access a great deal of information. Runtime performance is therefore greatly affected by the quantity of data and its representation. Intelligently choosing data structures can affect performance by orders of magnitude, and, at scale, it matters a lot. ...
Get Mahout in Action now with the O’Reilly learning platform.
O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.
