How Not To Sort By Average Rating

Post by [deleted] » Tue, Apr 03, 2012, 5:32 PM UTC

_delirium wrote: post #40

NathanRice wrote: post #22

While I agree with the spirit of the article, this is one of those cases where a Bayesian treatment is conceptually much clearer. Assume that ratings are being generated by a stable stochastic process where the underlying distribution is multinomial (ignoring the ordinal character of ratings, for the time being) and use a dirichlet conjugate prior. This gives you a posterior distribution over new ratings for an item.…

Here's a paper proposing a solution in that space, and which also compares itself to the article linked here (kind of nice to see... papers sometimes fail to cite stuff that's "only" posted online rather than properly published, even if the authors know about it and it's quite relevant): http://www.dcs.bbk.ac.uk/~dell/publications/dellzhang_ictir2... I emailed Miller a while ago to see what he thought of this reply,…

Post by NathanRice » Tue, Apr 03, 2012, 5:40 PM UTC

zbuc wrote: post #81

NathanRice wrote: post #22

While I agree with the spirit of the article, this is one of those cases where a Bayesian treatment is conceptually much clearer. Assume that ratings are being generated by a stable stochastic process where the underlying distribution is multinomial (ignoring the ordinal character of ratings, for the time being) and use a dirichlet conjugate prior. This gives you a posterior distribution over new ratings for an item.…

I would absolutely love to learn more. I've been trying to solve some novel NLP and machine learning problems lately but my lack of statistical knowledge is becoming apparent the further along I get. Do you have any recommendations for a good introductory treatment of Bayesian statistics?

Post by ashishb4u » Tue, Apr 03, 2012, 5:42 PM UTC

Post by _delirium » Tue, Apr 03, 2012, 5:54 PM UTC

_delirium wrote: post #40

NathanRice wrote: post #22

While I agree with the spirit of the article, this is one of those cases where a Bayesian treatment is conceptually much clearer. Assume that ratings are being generated by a stable stochastic process where the underlying distribution is multinomial (ignoring the ordinal character of ratings, for the time being) and use a dirichlet conjugate prior. This gives you a posterior distribution over new ratings for an item.…

Here's a paper proposing a solution in that space, and which also compares itself to the article linked here (kind of nice to see... papers sometimes fail to cite stuff that's "only" posted online rather than properly published, even if the authors know about it and it's quite relevant): http://www.dcs.bbk.ac.uk/~dell/publications/dellzhang_ictir2... I emailed Miller a while ago to see what he thought of this reply,…

Post by DanI-S » Tue, Apr 03, 2012, 6:03 PM UTC

dxbydt wrote: post #88

edw519 wrote: post #10

I love it and I hate it. Why I love it: It's precise. It's elegant. It's rigorous. It's based upon solid, proven science & theory. It's a perfect application for a computer. And most of all, it does what's intended: it works. Why I hate it: What human can understand it? I used to implement the first manufacturing and distribution systems that used thinking like this. They figured, "We finally have the horsepower to a…

>What human can understand it? Lets start with Wilson's midpoint, since that's just high school math. def mid(upvotes:Int, downvotes:Int) = { val total = upvotes+downvotes+0.0 val up = upvotes/total val half = 0.5 val a = total/(4+total) val b = 4/(4+total) a * up + b * half } So there are two weights a and b. Using these weights, the midpoint is a weighted average of half and the proportion of upvotes. It should be…