My earlier work with Social Book Club, and current work with Kirkus Reviews, has me spending a fair amount of time exploring and developing recommendation systems. There are a variety of good books and papers on the subject, but I recently finished reading “Mining of Massive Datasets” (a free ebook that accompanies a Stanford CS course on Data Mining), and it was a surprisingly good read.

The book covers a number of topics that come up frequently in data mining: reworking algorithms into a map-reduce paradigm, finding similar items, mining streams of data, finding frequent items, clustering, and recommending items. Unlike many texts on the subject, you won’t find source-code in this book; but rather, extensive explanations of multiple techniques and algorithms to address each topic. This lends itself to a better understanding of the theory, so that you understand the trade-offs you might be making when implementing your own systems.

There are easier texts to get through, but if you’re getting started with recommendation or data-mining systems, and haven’t read this book, I’d encourage you to do so.