Web 2.0 enhancement to the "Related Modules" block
| Project: | Drupal.org webmasters |
| Component: | Redesign |
| Category: | feature request |
| Priority: | normal |
| Assigned: | danithaca |
| Status: | needs work |
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We've been working on the d.o. "Related Modules" block for 2 years now. We have evaluated 9 alternative algorithms. Result shows that improving the algorithms increases click-through rate from 0.1% to 1.5%. We summarized the results and wrote a paper for ACM Recommender System Conference'09. Early access of the paper can be found at http://mrzhou.cms.si.umich.edu/node/139.
One conclusion from our study was that automatic algorithms, no matter how we refine it, had certain limitations. To further improve the quality of "Related Modules" block, perhaps we have to use Web 2.0 techniques. That is, to provide a textbox (w/ auto-complete) on the module pages for the users to suggest related modules. And then, we aggregate the suggestions using some smart algorithms to display in the "Related Modules" block.
An alternative might be to have the module authors add related links on their module page. However, this has 2 problems: 1) the authors don't necessarily know all the related modules. 2) they might be reluctant to add links to substitute modules.
If the community generally thinks this is a good idea, I can work on a prototype.
Your comments are appreciated!
#1
I think this needs to go into the redesign queue.
#2
Daniel, I think you should go ahead with this. I don't think adding another block for adding module recommendations is that cumbersome. We can test on drupal.org and see if it adds a lot of value.
#3
Thanks! I'll go ahead and try to implement it :)
#4
This is now deployed on staging8.drupal.org. Let's get the module recommendation blocks and the ability to recommend modules that are related added to that site.
#5
Explanation of the algorithm from my advisor Paul Resnick:
"...[This is] an integrated version that automatically adjusts which recommendations it makes based on user clickthroughs, through a technique called a Multi-Armed Bandit learning algorithm. All of the matching algorithms (based on text similarity, co-mention in conversations, or co-installation) generate initial candidate sets of items to recommend, but the actual items to recommend will adjust over time based on clickthroughs. the adjustments can happen offline, in batches, so they don't affect page load performance."