How to calculate correlation between consumers according their multi-aspect and overall rating? In e-commerce site, consumers would provide a overall rating with multi-aspect ratings. For example, if a consumer purchase a camera, he would provide a rating info as follows:
User    Product    Size/Wight    Appearance    Battery    Price    Display    Overall
A       Camera 1      4             2              3        5        5          4 
B       Camera 2      4             2              3        5        5          2
C       Camera 3      2             4              5        1        2          4
D       Camera 4      2             4              5        1        2          2

In practice, in our dataset, a consumer would only purchase a camera. So, how can we determine the correlation between pair of users (A vs B, or A vs C, etc) using the provided heterogeneous data.
 A: Cluster analysis might be your answer.  It is a family of methods used to determine the degree of similarity among different cases (in your instance, cases would mean either people or cameras).  There are many cluster analysis methods, and even within a given method there are many options or specifications to be chosen by the analyst.  10 different analysts might arrive at 10 different solutions.  So I can't give you a recipe that you can follow in order to achieve your goal; I don't think anyone can.
Multidimensional scaling is another approach to seeing how the different cases compare to one another.  (Most commonly it is applied to variables, but it can also map cases in N-dimensional space.)  If anything, though, it is even more technical than cluster analysis.
As you read up on these things, you might also want to look into perceptual mapping or the Galileo technique.
A: I think you are using the English meaning of correlation but looking for something known in literature as 'Association rule learning' - it shows how the buying options are related. e.g., if someone rates size/weight high are they equally likely to do so for batter too?? 
This won't work between individual users but will go over the entire data set and decide the association. The most common algorithm taught to beginners is A Priori and you may want to have a look at it to see if it suits your needs to start with. What @rolando2 suggests is also good - there are cluster algorithms like Birch that could help you with it too!
Hope this helps...
