The Michigan Library Association 2012 annual conference is taking place this week, and I was fortunate to attend the first day of the event.
Mary Kelly and I gave our Thingamabobs and Doodads: Tech Support IS Reference talk, which is one of my favorites. We set everything up, it looked great on the screen, we introduced ourselves, and when I pushed the button to go to the next slide...nothing. The computer locked up, then decided to shut down. No worries, though - I just kept talking while Mary rebooted the computer, and everything was fine. The irony of technology glitches during a technology talk was not lost on us, though.
I attended three really useful afternoon talks. Here are notes from the first.
Going Bananas for Appeal Factors in Multi-Type RA
Presenter: Kathryn Bergeron
Slides available here
I've known Kathryn for a few years now, and I think she's someone to watch closely. She's fun and interesting and smart, and I always enjoy her presentations. She said that this program was inspired by my multi-type advisory posts here, but she took the idea to a whole new level. I'll be posting a new multi-type advisory entry soon, using her method (which is WAY better than mine).
Kathryn's program introduced me to two new online sources for readers and other-format advisory, and showed me a new way of using even more.
If you go to Amazon.com and look up a title, you can see the links to "Customers that bought this title also bought..." Even though they're not necessarily related items, they do indicate that people who purchased one thing had some sort of interest in the others. That's one way to start formulating read-alikes (and watch-alikes and listen-alikes, etc.)
The database NoveList can be used to identify appeal factors. The database uses a controlled vocabulary, which is good because all items with the same appeal factors are identified and defined the same way. When you look up a title in NoveList, there is a menu on the right side of the screen with check boxes. Check the boxes you are interested in (such as tone or writing style) and then click the search box. The results are books identified with those same appeal factors. This same idea could be used with tags in LibraryThing and GoodReads, and in the Books and Authors database too.
Jinni is a great way to find movies and TV shows by appeal factors. Once you've identified the appeal factors in NoveList, you can try to match up those terms with Jinni. They don't use the same controlled vocabulary, so you have to stretch a bit, but Jinni has categories like mood, plot, place, and time period (among others) which can be matched up fairly well with the adjectives and appeal factors NoveList or other reviews used to describe the title.
This is a great way to discover music by appeal factors. You can find music that is dark or calm, energetic or positive. Then you can break it down further to time period and genre.
Metacritic is one of the few places that reviews video games. It also reviews movies, tv shows, and music. You can browse by genre, which is a great start. Metacritic doesn't use appeal factors, so it's not quite the same as the other sources we looked at. However, you can search for things like "adventure" or "time travel" and it will come up with some matches, so it is a useful source. It might be a better starting point for multi-type RA than a read-alike
source, since it would give you reviews with plenty of adjectives that
you could use in the other sources.