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https://issues.apache.org/jira/browse/MAHOUT-898?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13158654#comment-13158654
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Ted Dunning commented on MAHOUT-898:
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{quote}
Incidentally, we also tried loglikelihood as similarity metric (and a few other
ones); we set on Pearson because it worked best. This was before I measured
precision/recall, I'll probably now repeat the experiments to get those metrics
with log-likelihood to see what comes up.
{quote}
As you correctly noted earlier, precision@10 or precision@20 is a much better
measure of quality. It will be good to hear your results.
When you do test with log-likelihood, make sure you try with two strategies.
First with only positive votes as interactions and secondly with any vote as an
interaction.
> Error in formula for preference estimation in GenericItemBasedRecommender
> -------------------------------------------------------------------------
>
> Key: MAHOUT-898
> URL: https://issues.apache.org/jira/browse/MAHOUT-898
> Project: Mahout
> Issue Type: Bug
> Components: Collaborative Filtering
> Environment: mahout-core
> Reporter: Paulo Villegas
> Assignee: Sean Owen
> Priority: Minor
> Labels: patch
> Fix For: 0.6
>
> Attachments: GenericItemBasedRecommender.diff
>
>
> The formula to estimate the preference for an item in the Taste item-based
> recommender normalizes by the sum of similarities for items used in
> estimation. But the terms in the sum taken to normalize should be in absolute
> value, since they can be negative (e.g. when using Pearson correlation,
> similarity is in [-1,1]). Now they are not, and as a result when there are
> negative and positive values they cancel out, giving a small denominator and
> incorrectly boosting the preference for the item (symptom: it is easy for a
> predicted preference to take the maximum value, since the quotient becomes
> large and it is capped afterwards)
> The patch is rather trivial (a one-liner, actually) for
> src/main/java/org/apache/mahout/cf/taste/impl/recommender/GenericItemBasedRecommender.java
> Note: the same error & suggested fix happens in GenericUserBasedRecommender
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