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<p><!-- Make sure you modify the 4Cast title in this section -->
<span style="font-size: 20px; font-weight: bold;
color: rgb(0, 0, 0); font-family: arial;
line-height: 110%;">OPLIN 4Cast #251: Automating
readers' advisory</span><br>
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<span style="font-size: 11px; font-weight: normal;
color: rgb(102, 102, 102); font-style: italic;
font-family: arial;">October 12th, 2011</span></p>
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<p style="text-align: justify; font-size: 16px;
font-family: arial; line-height: 110%;"><a
href="http://www.oplin.org/4cast/wp-content/uploads/2011/10/reading_16161_sm.gif"><img
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height="131" width="154"></a>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?
</p>
<div> </div>
<ul style="text-align: left;">
<li style="text-align: justify; font-size: 16px;
font-family: arial; line-height: 110%;"><a
href="http://radar.oreilly.com/2011/09/evolution-of-data-products.html#discovery">The
evolution of data products</a> (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."</li>
<li style="text-align: justify; font-size: 16px;
font-family: arial; line-height: 110%;"><a
href="http://www.readwriteweb.com/archives/goodreads_book_recommendation_engine_launched.php">Using
20 billion data points, Goodreads will
recommend your next book</a>
(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."</li>
<li style="text-align: justify; font-size: 16px;
font-family: arial; line-height: 110%;"><a
href="http://publishingperspectives.com/2011/09/evolution-of-social-reading/">From
commentary to conversation: the evolution of
social reading</a> (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.'"</li>
<li style="text-align: justify; font-size: 16px;
font-family: arial; line-height: 110%;"><a
href="http://radar.oreilly.com/2011/10/hooked-on-context.html">Hooked
on context</a> (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."</li>
</ul>
<div style="text-align: left;"> </div>
<p style="text-align: left; font-size: 20px;
font-family: arial; line-height: 110%;"><small><strong><em>Book
data fact:</em></strong></small><br>
</p>
<div style="text-align: justify; font-size: 16px;
font-family: arial; line-height: 110%;">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.
</div>
<div style="text-align: left;"> </div>
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