Similarity-based Recommendation Engines by Josh Adell.
From the post:
I am currently participating in the Neo4j-Heroku Challenge. My entry is a — as yet, unfinished — beer rating and recommendation service called FrostyMug. All the major functionality is complete, except for the actual recommendations, which I am currently working on. I wanted to share some of my thoughts and methods for building the recommendation engine.
I hear “similarity” as a measure of subject identity: beers recommended to X; movies enjoyed by Y users, even though those are group subjects.
Or perhaps better, as a possible means of subject identity. A person could list all the movies they have enjoyed and that list be the same as a recommendation list. Same subject, just a different method of identification. (Unless the means of subject identification has an impact on the subject you think is being identified.)
Looks to me like these guys still have some ways to go before they can compete with the likes of Ratebeer.
Comment by larsga@garshol.priv.no — February 26, 2012 @ 4:12 am
True but how would you rate “compete?” Serious question.
Vendor: Recommendations lead to more sales?
User: Recommendations so I like what my friends like?
Expert: Recommendations so I like what other experts like?
The granularity at which recommendations can work and recent work on priming people to decide in particular directions look like opportunities/hazards depending on your point of view.
Oh, forgot:
Student: Recommendation of the cheapest brew with the most alcohol. 😉
Comment by Patrick Durusau — February 28, 2012 @ 10:27 am