Data Mining & Algorithms

The Evolution of the Virtual Salesperson: Using Behavioral Context + Rules to Deliver Relevant Product Recommendations

(NOTE: Earlier this year, RichRelevance and CNET announced their strategic partnership to integrate CNET’s rule-based Intelligent Cross Sell recommendations with RichRelevance’s behaviorally-based RichRecs™ offering to offer the industry’s first truly robust personalization solution for the world’s largest online retail sites. Patrick Monasterio is a member of the CNET/{rr} development team and is working with joint customers on integrating this new solution.)

Patrick MonasterioWhen done right, relevant product recommendations can deliver up to a 30 percent lift in conversion and sustain or even increase these levels over the long term.  Therefore, the multi-million dollar question for merchants (literally) is “What recommendations do I want to show? How do I do this?”  The most successful recommendation emulates the logic of a good salesperson by delivering the right product to a specific person for a specific situation.

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Ensemble Learning: Live on TV's Jeopardy and Behind the Scenes on Leading Retail Websites

Starting today, IBM’s Watson supercomputer will go up against a pair of human Jeopardy champions. Regardless of whether man or machine comes out on top, it will be a banner day for a machine learning technique called ensemble learning. Ensemble learning is based on the notion that tens or hundreds of independent algorithms, each aimed and working in a particular kind of context are better than one bigger, more complex algorithm. This idea lies at the heart of both Watson’s approach to generating Jeopardy questions and RichRelevance’s approach to generating relevant product recommendations and advertising.

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The Inspiration Behind RecLab: Don't Bring the Data to the Code, Bring the Code to the Data

Last fall I had the pleasure of visiting Barcelona, one of my favorite cities on earth. It normally wouldn’t take much beyond the great Catalan food and wine to entice me to visit, but in this case there was an even more compelling reason to visit—to participate in the ACM Recommender Systems conference and spend time digging into deep technical conversations with some of the leading scientists and engineers in the field. I also had a chance to demonstrate Instant Shopper, a neat little demo we put together to illustrate the speed and effectiveness of RichRelevance algorithms that combine search and behavioral data on some of our merchant partners’ sites.

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