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https://issues.apache.org/jira/browse/MAHOUT-305?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12836543#action_12836543
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Ankur commented on MAHOUT-305:
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Sean, Thanks for filing the jira. Nothing points from our discussion here.
1. Need to decide on the dataset to run both the implementations on. I have
netflix dataset in mind but a strange thing I observed during my tests with it
is that there were 2 - 3 users who rated more than 10,000 movies! This seemed a
little odd to me. Can you or some else who has had experience with the dataset
validate my observation ?
2. Both the implementations need to run on dataset in the identical environment
to gauge performance and accuracy. For accuracy I believe we need to do a
Precision-Recall test. My understanding of it is that
a) Do a 80-20 split of the data (80% train and 20% test) with split
happening on a timeline.
b) Feed training data to the algorithm and generate recommendations for a
subset of users from training data.
c) Compare those recommendations with items actually present in the
history of user in test data.
d) Calculate precision = tp / (tp + fp) = (recommendations actually
present in user's history) / (total items recommended)
e) Calculate recall = tp / (tp + fn) = (recommendations actually
present in user's history) / (total items in user's history)
f) Finally take a simple avg of both across all the users to get approx
global precision/recall.
please feel free to correct any of the step above if I misunderstood anything.
> 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
> 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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