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https://issues.apache.org/jira/browse/MAHOUT-1185?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13619589#comment-13619589
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Cunlu Zou edited comment on MAHOUT-1185 at 4/2/13 7:30 AM:
-----------------------------------------------------------
Please check the code carefully, there are two variables calcuated in the
processOneUser function, the average diffs (the variable *average* in the code)
calculated correctly as you said, but there is also another variable to
calculate the average preference value for *individual item* (the variable
*itemAverage* in the code), they are totally different. The itemAverage value
is used when no diffs values are avaible to predict the preference, for
example, suppose we have following user-pref matrix (a-c are users,A-C are
items)
| ||A||B||C|
|a||1||-||3|
|b||2||-||4|
|c||-||2||-|
for user c, we wanna predict the preference value for item C, since we only
know user c has the preference value for item B, but there is no diff value
available between B and C, in this case, the mahout tried to use the average
value for item C which is (3+4)/2=3.5 as the predict value for the item C. The
same case for user c to predict the preference value for item A. By comparing
the predicted values, we then recommend item C not item A to user c instead.
However, the code has the mistake for calculating this average value (*NOT the
DIFF value) as I stated in the previous comments, hope I made this clear.
was (Author: stevenzcl1):
Please check the code carefully, there are two variables calcuated in the
processOneUser function, the average diffs (the variable *average* in the code)
calculated correctly as you said, but there is also another variable to
calculate the average preference value for *individual item* (the variable
*itemAverage* in the code), they are totally different. The itemAverage value
is used when no diffs values are avaible to predict the preference, for
example, suppose we have following user-pref matrix (a-c are users,A-C are
items)
A B C
a 1 - 3
b 2 - 4
c - 2 -
for user c, we wanna predict the preference value for item C, since we only
know user c has the preference value for item B, but there is no diff value
available between B and C, in this case, the mahout tried to use the average
value for item C which is (3+4)/2=3.5 as the predict value for the item C. The
same case for user c to predict the preference value for item A. By comparing
the predicted values, we then recommend item C not item A to user c instead.
However, the code has the mistake for calculating this average value (*NOT the
DIFF value) as I stated in the previous comments, hope I made this clear.
> MemoryDiffStorage.class has a bug for slope one algorithm which could cause
> incorrect recommendation results
> ------------------------------------------------------------------------------------------------------------
>
> Key: MAHOUT-1185
> URL: https://issues.apache.org/jira/browse/MAHOUT-1185
> Project: Mahout
> Issue Type: Bug
> Components: Collaborative Filtering
> Affects Versions: 0.7
> Environment: Ubuntu
> Reporter: Cunlu Zou
> Assignee: Sean Owen
> Labels: patch
> Attachments: MemoryDiffStorage.patch
>
> Original Estimate: 10m
> Remaining Estimate: 10m
>
> The function processOneUser(long averageCount, long userID) in the
> MemoryDiffStorage.class file contains a bug for calculating the itemAverage.
> Since the function tried to calculate the average difference among items (in
> a nested loop) and also the average individual item preference value in the
> same loop (the loop only from 0 to length-2, *for (int i = 0; i < length - 1;
> i++)*), the itemAverage variable does not count the last item's preference
> value for every users which could lead to an incorrect recommendation results.
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