Hi, I've made a search, sorry in case this is a double post. Also, this question may not be directly related to Mahout.
Within a domain which is enitrely user generated and has a very big item churn (lots of new items coming, while some others leaving the system), what do you recommend to produce accurate recommendations using Mahout (Not just Taste)? I mean, as a concrete example, in the eBay domain, not Amazon's. Currently I am creating item clusters using LSH with MinHash (I am not sure if it is in Mahout, I can contribute if it is not), and produce recommendations using these item clusters (profiles). When a new item arrives, I find its nearest profile, and recommend the item where its belonging profile is recommended to. Do you find this approach good enough? If you have a theoretical idea, could you please point me to some related papers? (As an MSc student, I can implement this as a Google Summer of Code project, with your mentoring.) Thanks in advance -- Gokhan
