(Josh and I had spoken separately.) I think he's interested in perhaps learning those similarities, indeed. As a rough-and-ready start, I'd suggested pure collaborative filtering based on user and item associations only. Later, you can work in user-user similarity, learned elsewhere, to improve things.
2011/8/4 Christopher Jordan <cjor...@gilt.com> > I actually disagree with that statement. While Mahout is built on Hadoop, > distributed computing is not a factor in whether or not you can model your > data. > > Josh, it sounds like you already know a fair bit about your users. In that > case, why not leverage your demographic data to group them yourself using > domain knowledge that makes sense. For example, using the zip and age to > group them regionally and by age group. Then you can try to build a > recommendation engine for each group of users. If you don't know a lot about > your users to make those kinds of groups, it sounds like you might need to > do some exploratory statistics on them. > > On Aug 4, 2011, at 2:21 AM, 戴清灏 wrote: > > > Hi, > > I think the core issue is not how this engine work, but whether mahout > > fits your data size. > > Mahout is built on hadoop, which digest big data. > > If your data size is not that huge or incompatible with mapreduce > model, > > it may not be a good idea. > > Regards. > > Roger > > > > 2011/8/4 Josh Dulberger <jidulber...@gmail.com> > > > >> Hello, > >> > >> I have some familiarity with machine learning (in an academic setting) > but > >> am looking for some assistance on which Mahout algorithms would be suit > my > >> needs. > >> > >> I am doing consumer behavior research at a web-marketing startup, where > we > >> generate a decent amount of data. We track behavioral data - engagement > >> stats, view-times, feedback - and also have demographic data. We also > have > >> an inventory of items/sites, and some rudimentary (manual) > categorizations. > >> > >> We were just approved for a data warehouse to integrate our data and I > have > >> approval to begin working on a consumer targeting platform. The core > idea > >> is > >> to match consumers with items, testing different approaches for > different > >> classes of consumers and items. I expect to be looking at > item-similarity, > >> consumer-similarity, and hybrid models, and eventually incorporate > global > >> trends. > >> > >> Initially, I think we can start with a recommender engine, then develop > a > >> clustering/classifier. But I am now wanting more insight into what kinds > of > >> questions each is best at answering and how fit together. So far, my > >> understanding of the difference is that recommenders accept input of > users, > >> positive/negative scoring, item, and timestamp, then output a > >> recommendation > >> (with variation depending on the specific algo). > >> > >> This leaves out demographic data (age, gender, zip, or even > socioeconomic). > >> I gather that clustering algos can incorporate this kind of data (and > more) > >> in order to find natural groupings. Is the natural connection point to > find > >> similar users and items using clustering, then feed that into a > >> recommender? > >> How does this feeding work? Or, if I the above is at all right-headed, > what > >> are some options as to how to make the connection? > >> > >> I appreciate in advance any answers, ideas, insights, or even questions > any > >> of you may have. > >> > >> Thanks, > >> > >> Josh > >> > >
