I have no idea what is a good way to discover and learn new things. Surely “science of learning” has some models and advices that may have been useful, but I choose to follow the lazy way. To learn about collaborative filtering, I googled.

The first source of information reported by the search engine is (of course?) wikipedia: collaborative filtering .Then I passed 2 links in Google results (general information) to direct myself toward specialized articles through this great personal page. I navigate a bit in it and with the help of few more Google searches I got lost … on my way to be lost, at least I figure out some articles that seems fundamental: Reporting and evaluating choices in a virtual community of use, GroupLens: an open architecture for collaborative filtering of netnews, Social information filtering: algorithms for automating ‘word of mouth’. My machine learning background is appreciated at this stage.

But Wow! That’s a lot of information: do I really understood what I read? This was time to got dirty hands so I choose one system looking relatively simple and faced it at implementation level: Slope-One algorithm.

Then I found a recent article about Google news personalization recommender system: I still don’t feel comfortable to compare the different algorithms, but at least this great article put things in real context (taking into account the big scalability problem). And also this point me toward a wonderful survey: Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions.

I have enough (too much) information for now, so I guess that (1) it is why I feel a bit confused and (2) it is the good time to write down all that stuffs to try to organized it: field description and algorithms classifications, evaluation of recommender systems (see for example here and here) and at last but not least the scalability problem. And I will try to use citeUlike and its recommendations 🙂

Hopefully, after that, I can go further “Incorporating contextual information in recommender systems using a multidimensional approach” (available here), but before I still have to find a survey of available recommender systems. Any recommendation? 😉

Did I miss something important?

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