Hi Subutai, Thanks for the presentation. The problem I am trying to solve is different but related to predicting where a person browsing a website is likely to click next. It is so called "cold start" problem for recommendation system that actively learns from new user implicit feedback. The goal is to determine product ratings for a new user who just came and started browsing products on a web site for the first time. I think both spatial and temporal aspects are important here and HTM may help to classify user preferences. The main problem is that very little data is available both for new user and new product cold-start scenario.
On* Mon Feb 1 15:54:37 EST 2016*, *Subutai Ahmad* wrote: > Hi Dmitri, > > That paragraph refers to work I did way back in 2009/2010. We got a huge > amount of real web traffic data from the news site forbes.com and used it > to debug the very first versions of the Temporal Memory algorithms. I've > attached the presentation I gave at a workshop a while ago. (Note that it > uses our old product name Grok - you can ignore that and substitute "HTM".) > > Unfortunately we are not allowed to release the data. It would be great if > someone could find similar data from another company that we could actually > release as a dataset. > > --Subutai > > >
