Michael Schueppert

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When users search online for a business, the search engine may present them with a list of related business recommendations. We address the problem of constructing a useful and diverse list of such recommendations that would include an optimal combination of substitutes and complements. Substitutes are similar potential alternatives to the searched(More)
We present a secondary ranking system to find and remove erroneous suggestions from a geospatial recommendation system. We discover such anomalous links by "double checking" the recommendation system's output to ensure that it is both structurally cohesive, and semantically consistent. Our approach is designed for the Google Related Places Graph, a(More)
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