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https://issues.apache.org/jira/browse/MAHOUT-1445?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13968365#comment-13968365
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Nick Martin commented on MAHOUT-1445:
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Worked on this a bit over the weekend. Feel free to use some/all/none if folks
find it useful as an intro. I imagine the rest of the item based rec workflow
would be described in greater detail below this intro piece, but hopefully
something along these lines helps potential users get a feel for what's
possible "above the fold" before diving into data models and similarity
metrics, etc. etc.
***Proposed text below***
Item Based Recommender
Introduction
Mahout’s item based recommender is a flexible and easily implemented algorithm
with a diverse range of applications. The minimalism of the primary input
file’s structure and availability of ancillary filtering controls can make
sourcing required data and shaping a desired output both efficient and
straightforward.
Typical use cases include:
• Recommend products to customers via an eCommerce platform (think:
Amazon, Netflix, Overstock)
• Identify organic sales opportunities
• Segment users/customers based on similar item preferences
Broadly speaking, Mahout's item-based recommendation algorithm takes as input
customer preferences by item and generates an output recommending similar items
with a score indicating the likelihood a customer will "like" the recommended
item.
One of the strengths of the item based recommender is its adaptability to your
business conditions or research interests. For example, there are many
available approaches for providing product preference. One such method is to
calculate the total orders for a given product for each customer (i.e. Acme
Corp has ordered Widget-A 5,678 times) while others rely on user preference
captured via the web (i.e. Jane Doe rated a movie as five stars, or gave a
product two thumbs’ up).
Additionally, a variety of methodologies can be implemented to narrow the
focus of Mahout's recommendations, such as:
• Exclude low volume or low profitability products from consideration
• Group customers by segment or market rather than using user/customer
level data
• Exclude zero-dollar transactions, returns or other order types
• Map product substitutions into the Mahout input (i.e. if WidgetA is a
recommended item replace it with WidgetX)
The item based recommender output can be easily consumed by downstream
applications (i.e. websites, ERP systems or salesforce automation tools) and is
configurable so users can determine the number of item recommendations
generated by the algorithm.
> Create an intro for item based recommender
> ------------------------------------------
>
> Key: MAHOUT-1445
> URL: https://issues.apache.org/jira/browse/MAHOUT-1445
> Project: Mahout
> Issue Type: New Feature
> Components: Documentation
> Affects Versions: 1.0
> Reporter: Maciej Mazur
> Labels: documentation, recommender
> Fix For: 1.0
>
>
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