I'm looking at adding a rating system to my site, similar to that seen on Amazon. Basically users can rate the product out of 5 stars.
I've been racking my brains to think of the calculation to get the average customer rating... but I can't think how to do it.
And looking at Amazon's system, I think their caculation may be incorrect. If you take this page for example:
http://www.amazon.co.uk/exec/obidos...2521526-3543861
You can see that the average customer rating should actually be 5 stars as two people have chosen 5 whereas only one has chosen 4.
Any ideas??
You can calculate the average by taking the sum and dividing it by the number of values. In the case of ratings, it is the sum of all ratings divided by the number of ratings.
In the case you site, with ratings 5, 5 and 4, the average is (5+5+4)/3, which is 4.666..., and I'd guess they round to the nearest half yielding 4.5 out of 5.
why not try a jquery plugin?
http://orkans-tmp.22web.net/star_rating/index.html
Related
It appears my magento reviews are working out the star ratings out of 10 and not 5. I had 10 radio boxes strangely to begin with. After looking in the database I saw I had many entries in the ratings table. After deleting these extra entries, the user now enters star ratings using radio buttons out of 5. Which is now correct. However the star rating other customers see is being worked out still out of 10. So a star rating of 4/5 is showing as 40% on the stars, when it should be 80%. It is half what it should be. How can I change it so it stops working out the stars out of 10?
It's a fairly complex section of Magento, but if you start by poking around Mage_Review_Block_Product_View and Mage_Rating_Block_Entity_Detailed you should be able to find the code that generates the number that determines summary count. It sounds like it's dividing the review total (out of 100) by 10 (10 stars) rather than 20 (5 stars). Without seeing your code its difficult to help any further.
I need to calculate a star rating for a product
I know how to calculate the weighted average, but its not good enough
example (5*252 + 4*124 + 3*40 + 2*29 + 1*33) / (252+124+40+29+33) = 4.11
I want to avoid cases when a product get a 1000 five star ratings and one 4 star, and another one gets just one 5 stars and it gets on top
I know there is a way but i couldn't find it
thanks
Try sort you product record using new column (rating * votes).
it will help you to find the most voted product with best rating.
use sorting which can include number of ratings, something like number of votes divided by calculated avg rating.
You can multiply for a weight function, that gives a penalty to product with lower number of votes and converge in time. Something like this should do the job.
a parabole truncated to 1 should do the job
EX:
convergence_step=1000
if voters<convergence_step:
meanscore=score*{[(voters)/(float)(convergence_step)]^2}
else
meanscore=score
I'm designing a site and don't know how to rate the system in terms of logic.
Outcome is I want an item with 4 stars with 1000 votes to be ranked higher than an item with 1 vote of 5 stars. However, I don't want an item with 1 star with 1000 votes to be ranked higher than an item with 4 stars and 200 votes.
Anyone have any ideas or advice on what to do?
I found these two questions
Sorting by weighted rating in SQL?
MySQL Rating System - Find Rating
and they have their drawbacks and in the first one I don't understand what the winner means by "You may want to denormalize this rating value into event for performance reasons if you have a lot of ratings coming in." Please share some insight? Thank you!
Here's a quick sketch-up of such a system which works by defining a bonus factor xₙ for each flag number. According to your question you want:
x₄*4*1000 > x₅*1*5
and
x₁*1*1000 < x₄*4*200
Setting the factors to for example x₁=1, x₄=2 and x₅=2 will satisfy this, but you will of course want to adjust it and add the missing factors.
He means, you should put rating-data into the event-table (and thus have redundant data) to optimize it for performance.
See the wiki for Denormalization: http://en.wikipedia.org/wiki/Denormalization
The data you have to determine the rank of items is:
average rating
number of ratings
The hard part is probably to make rules for the ranking. Like: If the average rating for an item > 4 and the number of ratings < 4 treat it like rated 3.9
For convenience, I would put this value (how to treat the items for ranking) in the item-table.
EDIT: Im sorry guys my explantion of the problem wasn't clear! This should be better:
User sends ID numbers of articles and the max. number of bundles(packages)
API searches for all prices available for the articles and calculates best result for min. number of bundles (limit to max. number provided by customer)
ONE Bundle is one package of items delivered to ONE platform(buyer)
Thanks!
This is a fun little problem. I spent a few hours on it this morning, and while I don't have a complete solution, I think I have enough for you to get started (which I believe was what you asked for).
First of all, I'm assuming these things, based on your description of the problem:
All buyers quote a price for all the items
There's no assumption about the items, they may all be different
The user can only interact with a limited number of buyers
The user wants to sell every item, each to one buyer
The user may sell multiple items to a single buyer
Exact solution -- brute force approach
For this, the first thing to realize is that, for a given set of buyers, it is straight forward to calculate the maximum total revenue, because you can just choose the highest price offered in that set of buyers for each item. Add up all those highest prices, and you have the max total revenue for that set of buyers.
Now all you have to do is make that calculation for every possible combination of buyers. That's a basic combinations problem: "n choose k" where n is the total number of buyers and k is the number of buyers you're limited to. There are functions out there that will generate lists of these combinations (I wrote my own... there's also this PEAR package for php).
Once you have a max total revenue for every combination of chosen buyers, just pick the biggest one, and you've solved the problem.
More elegant algorithm?
However, as I intimated by calling this "brute force", the above is not fast, and scales horribly. My machine runs out of memory with 20 buyers and 20 items. I'm sure a better algorithm exists, and I've got a good one, but it isn't perfect.
It's based on opportunity costs. I calculate the difference between the highest price and the second highest price for each item. That difference is an opportunity cost for not picking the buyer with that highest price.
Then I pick buyers offering high prices for items where the opportunity cost is the highest (thus avoiding the worst opportunity costs), until I have k - 1 buyers (where k is the max I can pick). The final choice is tricky, and instead of writing a much more complicated algorithm, I just run all the possibilities for the final buyer and pick the best revenue.
This strategy picks the best combination most of the time, and if it misses, it doesn't miss much. Its also scales relatively well. It's 10x faster than brute force on small scales, and if I quadruple all the parameters (buyers, buyer limit, and items), calculation time goes up by a factor of 20. Considering how many combinations are involved, that's pretty good.
I've got some code drafted, but it's too long for this post. Let me know if you're interested, and I'll figure out a way to send it to you.
This is a graph problem. It can be solved with the Edmond's Blossom V algorithm. It's a matching algorithm to find the best pairwise matching for example in dating programs. Maybe you want to look for the 1d bin-packing algorithm. In 1d bin-packing you have a limit items to assign to unlimited boxes or shelves the better the boxes get filled.
If I understand the problem correctly, it is NP-complete via reduction from Minimum Set Cover. We can translate an instance of Set Cover into an instance of the OP's problem as follows:
Let an instance of Set Cover be given by a set X of size n and a collection of subsets S_1, S_2, ..., S_m of X. Construct an instance of the OP's problem where the seller has n items to sell to m buyers, where buyer i offers a price of 1 for item j if *S_i* contains item j and 0 otherwise. A solution to the OP's problem where the number of buyers is limited by k and the total price paid is n corresponds to a solution to the original Set Cover problem with k sets. So, if you had a polynomial-time solution to the OP's problem, you could solve Minimum Set Cover by successively solving it for the case of 1, 2, 3, etc... buyers until you found a solution with total price equal to n.
I have a newssystem where you can rate News with 1 to 5 stars. In the Database i save the count, the sum and the absolute rating as int up to 100 (for html output, so 5 stars would be 100 1 star would be 20percent.
Now i have three toplists:
Best Rated
Most viewed
Most commented
Last two ones are simple, but the first is kinda tricky.
Before i took that thing over it was all a big mess, and they just put the 5 best rated news there, so in fact if there was a news rated 4.995 with 100k votes and another one with 5 stars at 1 vote, the "better rated" one is on top even if that is obv ridiculous.
For the first moment i capped the list so only news with a certain amount of votes (like 10 or 20) can be in the list.
But i do not really like that. Is there a nice method to kind-a give those things a "weight" with the count or something like that?
Have you considered using a weighted bayesian rating system? It'll weight the results based on the number of votes and the vote values themselves.
You could explore the statistical confidence in the rating perhaps based around the average rating received for all entries and the standard deviation of all votes. While an entry has an average rating of 5, if you only have a few votes then you may not be able to say with more than 90% confidence that the actual rating is above 4.7 say. You can then rate the entries based upon the rating for which you have 90% confidence.
I'm not sure if this meets your requirement of being simple.
You could use median of the user ratings as the total rating.
You would have five fields with eatch article, each one containing how many times the article was rated as n stars. Then you would select the field with the biggest value of all these and that would be your rating. It has the advantage of ignoring the outliers in the ratings.