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https://issues.apache.org/jira/browse/MAHOUT-305?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12836725#action_12836725
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Ankur commented on MAHOUT-305:
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Typically when doing train-test data split, we divide the data on a timeline.
So as a simple example if we have 10 days data then we would keep last 2 days
data as test data and remaining as training data. If we remove all 5 star
rating the crude way, we may not be able to ensure this condition, not a hard
one but still a best practice AFAIK. Also I am not sure if 5 star ratings
would be 20 or even 10% of the total data.
The crude way you mentioned is ok for a start but I am not sure if its a fair
evaluation or not. Also with this we would effectively be calculating precision
as
precision = (5 start recommendations actually present in user's history) /
(total 5 star recommendations)
recall = (5 start recommendations actually present in user's history) / (total
5 start items in user's history)
is that what you mean?
> Combine both cooccurrence-based CF M/R jobs
> -------------------------------------------
>
> Key: MAHOUT-305
> URL: https://issues.apache.org/jira/browse/MAHOUT-305
> Project: Mahout
> Issue Type: Improvement
> Components: Collaborative Filtering
> Affects Versions: 0.2
> Reporter: Sean Owen
> Assignee: Ankur
> Priority: Minor
>
> We have two different but essentially identical MapReduce jobs to make
> recommendations based on item co-occurrence:
> org.apache.mahout.cf.taste.hadoop.{item,cooccurrence}. They ought to be
> merged. Not sure exactly how to approach that but noting this in JIRA, per
> Ankur.
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