I am researching recommender and rating systems for two clients at the same time for completely different reasons. One client wants to be able to be at a single or list of products or categories and recommend similiar or logical cross selling opportunities. The other client wants to be able to allow a user to define a setting that is ok and then give the user alternate settings that are algorithmically similiar but possibly superior to the original settings.
Here are some things I have run into while taking a quick look on the web.
(Common terminology: Recommendation Engine, Recommender Engine, Collaborative Filtering)
If you are trying to get targeted recommendations and cross selling products these two products are interesting to look at:
SUGGEST Karypis lab at the Univ. of MN (like Amazon's, "You might also like" engine)
RECOMMENDER ENGINE 3 United
If you are trying to build a user defined rating of products or services take a look at:
CoFE IIS Research Group of Oregon State(like Yahoo's news article ranking system)
Often companies will combine these two technologies to allow users to refine the products and services recommended to them.
For example: A baker views flour and sugar and is recommended yeast, flavorings and preservatives. Let's say the baker is an all natural baker and can rate the preservatives as something he would never use.
To see a site that uses both go to Betterpropaganda.com
The home page has recommendations based on user ratings. If you click on a specific artist or album you will see a list of Similar Music. This site uses a content based recommendation engine from Loomia.