2011/2/22 António Lemos <[email protected]>: > My first question is basically, if Mahout can be executed in real-time or as > a batch process?
Both. The "Taste" bit is all real-time. (Of course, you can use a real-time engine to generate recommendations in a background batch process if you like.) There is also a Hadoop-based implementation, which is quite separate. That is much more scalable but is necessarily a batch-oriented process, not real-time. > - Produce recommendations based on explicit rules or explicit > profile data; > > - Create profiles based on user browsing and use that information > in suggestion rules (e.g. If a specific user reads articles about Alex > Ferguson I can infer that he is a Manchester United supporter and thus the > recommendation engine will recommend articles related to Man. Utd.); > > - Produce recommendations based on content views or research; > > - Collaborative filtering capabilities and real-time analytic > models; These aren't really specific enough to say much about. The question is really, does Mahout have enough hooks and extension points to add in the business-specific logic you mention above? yes, I think you'll find there is a way to implement your logic within the framework. For example, it's up to you to decide how "content views or research" translates into user-item preferences. That's not something Mahout can do for you. But, given those preferences, it can make recommendations easily. > Do you know if Mahout addresses all these requirements? I’m searching for > articles related to Mahout integration (in > https://cwiki.apache.org/confluence/display/MAHOUT/MahoutIntegration this > section is blank) namely integration with the WCM we are going to propose > (EZ Publish) but I was not able to find anything, do you have any insights > about how does Mahout integrates with WCM solutions? There is no particular integration with any content management system, but I don't know what particular support would be appropriate. It runs as a Java process, or a web-based service accessed over HTTP. That can be accessed from just about anything. > My last doubt, - I think is probably related to the previous one, - is > basically a question about Mahout integration with user session logging. > Let’s say an anonymous user browses article A1 and A2 and rates item I1 and > I2, at this point the recommendation engine should be able to produce some > recommendation for related articles and items (explicit or not). What > happens to the user preferences when anonymous user decides to login in the > website? Are they included in the data model and thus related to that user > or they are lost? That is more up to your application than the engine. Anonymous users' actions are still identifiable by cookie, for instance. You can create them temporarily in the data model and recommend for them, yes. You can merge two users later, with a bit of code. But that is mostly a function of your web app, not the engine.
