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https://issues.apache.org/jira/browse/MAHOUT-103?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12781838#action_12781838
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Ankur commented on MAHOUT-103:
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For this co-occurrence based recommender I am planning to write a set of
map-reduce jobs that compute recommendations for users as folllowing:-
1. Take user's item history
2. for each item in his history fetch the top-N similar items. (Similarity
based on co-occurrence)
3. Add the co-occurrence scores if an item appears more than once (NOT weighted
avg). Consider an e.g. user-history { M1, M2, M3 } and top - 3 similar movies
for each of these along with co-occurrence scores
M1 -> (A, 5), (B, 4), (C, 2)
M2 -> (D, 6), (E, 3), (F, 2)
M3 -> (G, 8), (C, 5), (B, 2)
So the final scores in decreasing order will look like
(G, 8)
(C, 7)
(B, 6)
(D, 6)
(A, 5)
(E, 3)
(F, 2)
The idea I want to capture is that a candidate item gets higher score if its
similar to more items in user's click history.
Do you see any issue with this approach ? Any other better approach that you
can think of ?
As for the precision-recall test, I am still trying to see how to divide the
data in 'train' and 'test' for a fair evaluation. How do we do it in the
existing code ?
> Co-occurence based nearest neighbourhood
> ----------------------------------------
>
> Key: MAHOUT-103
> URL: https://issues.apache.org/jira/browse/MAHOUT-103
> Project: Mahout
> Issue Type: New Feature
> Components: Collaborative Filtering
> Reporter: Ankur
> Assignee: Ankur
> Attachments: jira-103.patch, mahout-103.patch.v1
>
>
> Nearest neighborhood type queries for users/items can be answered efficiently
> and effectively by analyzing the co-occurrence model of a user/item w.r.t
> another. This patch aims at providing an implementation for answering such
> queries based upon simple co-occurrence counts.
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