Fwiw, here are two light-weight feature-based recommendation engines I built for Node.js (for situations where you have the cold-start problem and therefore can't rely on user/item based collaborative filtering): Alike [1] and Look-Alike [2]<p>[1] <a href="https://github.com/axiomzen/Alike" rel="nofollow">https://github.com/axiomzen/Alike</a><p>[2] <a href="https://github.com/axiomzen/Look-Alike" rel="nofollow">https://github.com/axiomzen/Look-Alike</a>
So hang on, what exactly is a recommendation engine?<p>They give examples of LinkedIn (<i>people you may know</i>) and Amazon (presumably <i>other people who bought this</i>, <i>so-and-so's list of such-a-subject books</i>).<p>That makes sense, though the segment of businesses that may actually benefit seems limited. Social stuff, sure. Most of us? What's the minimum recommendable-entity/category-or-user threshold that this makes sense for? Is success with these sorts of engines merely a reflector of poor UI design in your normal UX? (Of the above examples, the first seems very unidimensional - in that it's basically a simple graph distance - and the latter also rather rudimentary and often irrelevant).<p>So what exactly is this thing providing? Graph analysis? I think not. It reads more like some kind of raw timestamped user behavioural event data processing to infer relationships between users or products they interact with. Reading through the docs it seems this is a layer on top of Apache Pig (<a href="https://pig.apache.org/" rel="nofollow">https://pig.apache.org/</a>) - <i>a high-level language for expressing data analysis programs, coupled with infrastructure for evaluating these programs</i>. I think clarity in explaining this thing could be improved, particularly selling clearly what a recommendation is and when its useful. Using phrases like "award winning" doesn't help.<p>PS. Why all the downvotes? Sheesh.
FWIW we've been using the mortar platform to run large pig jobs without a fuss at <a href="http://datadog.com" rel="nofollow">http://datadog.com</a> and we've been very happy with it. Glad to see them contribute their recommender code too.
Anyone know of any comparisons between this and Apache Mahout? I've used Mahout's Item-Item recommender in the past, and it's worked well, just wondering if there were advantages to this recommender.
WOW, Awesome Documentation and Product!! Kudos and Greetings from Germany 😊<p>Those who know what Hadoop, Pig and the whole "Data Science Stack" is, will find this surely useful.