The standard approach is to re-run the off-line learning. It is possible, though not yet supported in Mahout tools, to do real-time updates.
See here for some details: https://www.mapr.com/resources/videos/fully-real-time-recommendation-%E2%80%93-ted-dunning-sf-data-mining On Fri, Jun 19, 2015 at 2:35 AM, James Donnelly <[email protected]> wrote: > Hi, > > First of all, a big thanks to Ted and Pat, and all the authors and > developers around Mahout. > > I'm putting together an eCommerce recommendation framework, and have a > couple of questions from using the latest tools in Mahout 1.0. > > I've seen it hinted by Pat that real-time updates (incremental learning) > are made possible with the latest Mahout tools here: > > > http://occamsmachete.com/ml/2014/10/07/creating-a-unified-recommender-with-mahout-and-a-search-engine/ > > But once I have gone through the first phase of data processing, I'm not > clear on the basic direction for maintaining the generated data, e.g with > added products and incremental user behaviour data. > > The only way I can see is to update my input data, then re-run the entire > process of generating the similarity matrices using the itemSimilarity and > rowSImilarity jobs. Is there a better way? > > James >
