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Assessment of Conversation Co-mentions as a Resource for Software Module Recommendation

This paper is submitted to ACM Recommender System Conference'09, co-authored with my advisor Prof. Paul Resnick.

ABSTRACT

Announcing my GSoC 2009 project -- Making Drupal Smart: The Recommender Bundle

My Google Summer of Code 2009 proposal was accepted. The basic idea is to develop at least three modules based on Recommender API. For example, one module is to recommend Flash videos based on users' viewing history like in YouTube. A mockup screenshot is like this:

For more details and discussion, please go to http://groups.drupal.org/node/19894.

Gaming recommender systems for fun and profit

There's a big demand from the Drupal community to add fivestar-like ratings to the contrib modules. This would be a pretty cool feature, but it has other concerns too.

Roadmap for the pivots_block module recommendation on d.o.

A brief history to begin with ...

What's pivots_block?

The idea is to generate "related modules" recommendation based on co-citations. Suppose we have TinyMCE and FCKeditor co-mentioned together in many forum discussions, then we consider the 2 modules related to some extend. Here is a detailed explanation.

Where we are now?

Announcing the "Recommender API" module

From the experience of developing the "pivots" Drupal module recommendation system, I developed the general purpose Recommender API module. It was released today.

"Related module" recommenations based on project_usage.

Previously, 'related modules' were generated based on discussions in d.o. forum -- if several modules were mentioned in the same discussion threads, we consider them to be somewhat related. (More detailed explanation of the algorithms can be found in my previous Planet Drupal blogs).

Recent GA results for "pivots_block" module recommendation system

From the last Google Analytics (GA) study on the usefulness of "pivots_block" on 4 recommendation algorithms, we learned that the classical "relevancy" algorithm generated the better results. Therefore, we used the relevancy algorithm on D.O. from Dec/4/2008 to Jan/9/2009. And the average click-thru rate was 0.474%.

Pivots module recommendation system Google Analysis results

We developed 4 module recommendation algorithms and tested them on Drupal.org. And we used Google Analytics and tracked the click-through rates. The overall click-through rate was 0.263%, co-occurrences 0.097%, relevance 0.141%, recency 0.114% and uniqueness 0.138%. The relevancy algorithm appeared to have the highest click-through rate, but it was only significantly higher than the co-occurrences algorithm.

Pivots algorithms: An explanation of recommendations block on Drupal.org

You might have noticed that the pivots_block is enabled on drupal.org module pages, such as http://drupal.org/project/i18n. This blog is trying to explain the algorithms we are using in the pivots recommendation system.

Diagram of the pivots recommendation system on d.o.

This is the diagram to illustrate our proposed pivots recommendation system deployment on d.o. To see the description in words, please refer to here.
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