She considers Netflix, Pandora, and Project Steve (a museumgoers' art classifying tool that I hadn't heard of previously) in an exploration of how to make the best Readers' Advisory database. Basically, ideas I was pondering in a previous post with a lot more meat and expert opinion.
She concludes:
I have to apologize in advance for a hiatus here. I've been reading a lot of nonfiction for work and have let my pleasure reading slip. I shall return with verve next month, I hope!
RA has the human experts; what we need now is a database that manages to meld rich RA-infused data with an algorithm that lets us use it as we will.
If the day comes when a reader can open an RA database, input the title of a beloved book, and get back a list of suggestions that was collaboratively developed based on appeal, a range of expert input, and the books other readers suggest who also loved that title, then we will be well on our way to a database that supports our work.
Now we just have to get more people to be computer scientists/readers' advisors.
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