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Creating a Real-Time Recommendation Engine using Modified K-Means Clustering and Remote Sensing Signature Matching Algorithms

David Lippa
RealMassive, Inc.

Jason Vertrees
RealMassive, Inc.

Abstract

Built on Google App Engine (GAE), RealMassive encountered challenges while attempting to scale its recommendation engine to match its nationwide, multi-market expansion. To address this problem, we borrowed a conceptual model from spectral data processing to transform our domain-specific problem into one that the GAE's search engine could solve. Rather than using a more traditional heuristics-based relevancy ranking, we filtered and scored results using a modified version of a spectral angle. While this approach seems to have little in common with providing a recommendation based on similarity, there are important parallels: filtering to reduce the search space; independent variables that can be resampled into a signature; a signature library to identify meaningful similarities; and an algorithm that lends itself to an accurate but flexible definition of similarity. We implemented this as a web service that provides recommendations in sub-second time. The RealMassive platform currently covers over 4.5 billion square feet of commercial real estate inventory and is expanding quickly.

Keywords

algorithms, clustering, recommendation engine, remote sensing

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