Dan Kegel wrote: > On Sun, Mar 16, 2008 at 8:53 AM, Roderick Colenbrander > <[EMAIL PROTECTED]> wrote: > >> Personally I don't trust appdb regressions much. > > We could work around some of the problems by only listing > apps where the same reviewer gave it a lower rating in a > newer version of wine. That compensates for the lack > of a uniform rating system somewhat.
Reminds me of a bit I just read on a guy who's doing really well in the Netflix competition. One of the good heuristics he uses is to track the levels that a particular person uses to adjust for the "anchoring effect". http://www.wired.com/techbiz/media/magazine/16-03/mf_netflix?currentPage=all > One such phenomenon is the anchoring effect, a problem endemic to any > numerical rating scheme. If a customer watches three movies in a row > that merit four stars — say, the Star Wars trilogy — and then sees > one that's a bit better — say, Blade Runner — they'll likely give the > last movie five stars. But if they started the week with one-star > stinkers like the Star Wars prequels, Blade Runner might get only a 4 > or even a 3. Anchoring suggests that rating systems need to take > account of inertia — a user who has recently given a lot of > above-average ratings is likely to continue to do so. Potter finds > precisely this phenomenon in the Netflix data; and by being aware of > it, he's able to account for its biasing effects and thus more > accurately pin down users' true tastes. Jim
