I’ve just used blam! in this review of Revolution OS.
Basically blam! add some semantic information to your blog entry when this is a review. The semantic information can be understood by a computer program so that it will be possible to, for example, aggregating all the reviews about a certain book or movie.
Read about OpenReviews and their possible uses from Accordion Guy.
I’m planning to do something similar for my project CoCoA.
The semantic format for reviews is RVW (Review Module for RSS 2.0), created by Alf Eaton. Read an explanation of RVW from Corante.
The RVW specification is a module extension to the RSS 2.0 syndication format. RVW is intended to allow machine-readable reviews to be integrated into an RSS feed, thus allowing reviews to be automatically compiled from distributed sources. In other words, you can write book, restaurant, movie, product, etc. reviews inside your own website, while allowing them to be used by Amazon or other review aggregators.
There should be more than enough RVW metadata out there floating around at this point. The next step is for someone to build a decent aggregator that collates reviews of a particular topic or two. Because of RVS, creating aggregate rating scores and summarizing opinions should be very straightforward. It’s really not in the best interests of Amazon, epinions and the like to lose control of their review content, but RVW makes controlling review content impossible in the long term. Anyone got some pull at the Google skunkworks?
In the case of item types that describe reviews, overall average ratings on any particular product are easy to look up. However, if you choose to provide a description of your personal web of trust to those interfaces (think of blogrolls as a proto-example), you can efficiently get a sense of what your tribe of like-minded individuals thinks of that product. It’s the microblogosphere idea again – look up Recommender systems and the microblogosphere for more.
This is essentially what my PhD Research Proposal: Trust-aware Decentralized Recommender Systems is about.