Amazon’s Book Recommendation System is Quirky

Like many others, I have an Amazon.com wishlist, allow them to track my purchases, and occasionally tell them that I own a particular book if it is recommended to me.  Amazon uses this along with purchase sets made by other customers to generate “better” recommendations for books I might find interesting.  One thing its recommendation algorithm doesn’t seem to take into account is general subject knowledge – it knows of subcategories, and purchases of multiple items by the same people, but has no concept of actual relationships between books in a category.  This is actually a pretty reasonable thing to omit since there’s a great deal of such knowledge which could be taken into account, and the work/reward ratio for maintaining that is probably not great.  But as a result, I’ve had some rather interesting recommendations.

Also, I’m linking to books for clarity, but there’s no Amazon referral crap going on.

Mathematics texts.  Books on my Amazon wishlist: Foundations of Mathematical Logic, The Calculi of Lambda Conversion, Mathematical Foundations of Information Theory, Categories for the Working Mathematician, Basic Category Theory for Computer Scientists, among others.  Some of the more amusing mathematics recommendations from Amazon: Pre-Algebra, Algebra 2.

Literature.  Books on my Amazon Wishlist: Going After Cacciato, Letters to a Young Poet, The Norton Anthology of Poetry, ‘Tis: A Memoir, The Dharma Bums, among others.  Amazon recommends: Vocabulary Workshop: Level C.

These are just the few that I dug up spending a couple minutes searching my emailed recommendations; I’ve seen plenty more out of place recommendations upon logging into Amazon.com.  While it’s certainly true that some people will buy books at both extremes of a field’s complexity (perhaps something advanced for themselves or a friend, and something basic for a child), it’s still an amusing quirk to have this disparity with recommendations for an individual because of the presentation.  Amazon.com presents recommendations as a set of items you should be interested in according to Amazon’s “understanding” of your personal interests, when it’s really just correlating your purchases and explicit interests with the purchases of other users.