Announcement The Coop Page / Article Rating & Review Project!

[Nifty-Chicken]

I can go to my profile page then click on recent activity and then scroll through and count the ones I have done but I am NOT doing that.

LOL, yeah, don't waste your valuable rating-time on that!

By my count, here are the current rock-stars of rating pages!

Hope Hughes 45
BlackHackle 34
KikisGirls 33

 

[BlackHackle]

 

[Rose Quartz]

I'll have to catch up tomorrow. I'm headed to bed.

 

[Nifty-Chicken]

Great work peeps! We're down to 2,744 with zero ratings!

 

[BlackHackle]

 

[Nifty-Chicken]

As a reminder: Please be somewhat critical / objective with your ratings. This project becomes less valuable if peeps give every page a Five-Star rating.

If you want to give a lower-star review, but don't want your name listed, go ahead and check "anonymous" while submitting your rating.

 

[BlackHackle]

As a reminder: Please be somewhat critical / objective with your ratings. This project becomes less valuable if peeps give every page a Five-Star rating.

If you want to give a lower-star review, but don't want your name listed, go ahead and check "anonymous" while submitting your rating.

Thanks for the info, I am judging the coops on efficiency, design, space, and ease of access.

 

[Cryss]

I understand this clearly!!!
Go Bayesian Average!

To explain to those who want a simple English explanation:
A traditional average is done by taking all sets of data (in this case stars) divided by the total number of sets of data (in this case reviews)
In a traditional average an example would go like this:
You have two articles. Article 1 and Article 2.
Article 1 has 1 rating of 5 stars with an average rating of 5
Article 2 has 50 ratings with an average rating of 4.5
Article 1 would appear above Article 2 based on average rating. But would you rather have Article 2 above Article 1?
If yes, that is where the Bayesian average comes into play.

The Bayesian average computes the average (for this case average rating) using two extra variable, m and C.
For our purposes, our parent equation would look like this:
Rating of article = C x m + total number of stars/ C + number of reviews

m represents the predicted average rating of stars. So for us, we have a maximum of 5 stars. The average rating of 5 stars is 3.
So, m=3

C represents the value that is proportional to the typical data set. The typical data set is 5 stars.
So, C=5

Back to our articles.
Rating of Article 1= 5 x 3 + 5 x 1/ 5 + 1
Article 1 has an average rating of 3.3
Note: (5 x 1) comes from there being an average rating of 5 stars from 1 review. Therefore, the total number of stars is 5; 5 x 1
(5+1) comes from there being a C=5 plus the total number of reviews; 1

Rating of Article 2= 5 x 3 + 4.5 x 50/ 5 + 50
Article 2 has an average rating of 4.36

Using the Bayesian average, Article 2 now has a higher rating than Article 1

Simple English?

 

[Nifty-Chicken]

PROGRESS!!

 

[BlackHackle]

PROGRESS!!

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