[OPLIN 4cast] OPLIN 4Cast #251: Automating readers' advisory

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OPLIN 4Cast

OPLIN 4Cast #251: Automating readers' advisory
October 12th, 2011

http://etc.usf.edu/clipart/16100/16161/reading_16161.htm 
<http://www.oplin.org/4cast/wp-content/uploads/2011/10/reading_16161_sm.gif>Readers' 
advisory has long been an important component of the librarian's job. 
People coming to libraries expect the librarian to be able to recommend 
a next book to read, and while much of successful readers' advisory 
depends on skillfully leading the reader through a conversation that 
will reveal their interests, it's also very useful if the librarian has 
access to databases that link similar books to help her/him make some 
targeted recommendations. Lately, several services on the web are trying 
to improve their book databases to the point where they could be more 
effective, self-contained tools for helping people discover new books to 
read, in effect automating the readers' advisory process. Will they be 
successful?

    * The evolution of data products
      <http://radar.oreilly.com/2011/09/evolution-of-data-products.html#discovery>
      (O-Reilly Radar/Mike Loukides) "Discovery is the key to building
      great data products, as opposed to products that are merely good.
      The problem with recommendation is that it's all about
      recommending something that the user will like, whether that's a
      news article, a song, or an app. But simply 'liking' something is
      the wrong criterion. [...] I need software to tell me about things
      that are entirely new, ideally something I didn't know I'd like or
      might have thought I wouldn't like. That's where discovery takes
      over."
    * Using 20 billion data points, Goodreads will recommend your next
      book
      <http://www.readwriteweb.com/archives/goodreads_book_recommendation_engine_launched.php>
      (ReadWriteWeb/John Paul Titlow) "When most people hear 'the
      Netflix of book recommendations' they tend to think of another
      Internet giant known for its powerful recommendation engine:
      Amazon. Goodreads says it can provide better book recommendations
      than Amazon can because it has more data about what people
      actually like and dislike, as opposed to just purchases, browsing
      history and ratings."
    * From commentary to conversation: the evolution of social reading
      <http://publishingperspectives.com/2011/09/evolution-of-social-reading/>
      (Publishing Perspectives/Matteo Berlucchi) "Imagine therefore a
      Wikipedia style service which allows any reader to create a topic,
      add a collection of relevant books to that topic and let everyone
      else add more relevant books while also ranking the most
      interesting ones in order of preference. This 'reader-generated'
      topic system could grow to offer multiple ways to discover books
      by simply letting people browse these 'virtual tables.'"
    * Hooked on context
      <http://radar.oreilly.com/2011/10/hooked-on-context.html>
      (Interview with Valla Vakili/Jenn Webb) "We go through and create
      a graph for all of the little things inside of the books - the
      things that lead you off to new places - and then we show you all
      of the books that share those same elements. Once you've read the
      book, you can decide that you just want to go get the music, or
      you can decide to go get the music and then discover other books
      that have similar kinds of music in them. It's two types of
      discovery: The first takes you deeper into the world of the thing
      you're already in - places and things and such - and the second
      leads you toward books like the one you're reading based on the
      objects that we're graphing."

*/Book data fact:/*

Any good book recommendation web service will depend on massive amounts 
of data about massive numbers of books. More than 300,000 books are 
published each year, and self-published ebooks will quickly drive that 
number even higher.
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