James, From my days at the university I remember reinforcement learning ( https://en.wikipedia.org/wiki/Reinforcement_learning ) I suspect reinforcement learning is interesting to explore in the problem of e-commerce recommendation. My academic stuff is really rusted, but it's one of the few models that represent well the synchronous/asynchronous problem that we see in e-commerce systems... The models I'm seeing with Mahout + Solr (by MapR et alli) have Solr do the work to calculate the co-occurrence indicators. So the fact Solr is indexing this 'from scratch' during offline learning 'throws the whole model into the garbage soon' and doesn't leave room for the optimization/reward step of reinforcement learning. I don't know if someone could go on the theoretical side and tell us if perhaps there's a 'mapping' between the reinforcement learning model and the traditional off-line training + on-line testing. Maybe there's a way to shorten the Solr indexing cycle, but I'm not sure how to 'inject' the reward in the model... just some thoughts...
cheers Gustavo On Fri, Jun 19, 2015 at 5:35 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 >
