Abstract: This invention deals with the next generation improvements in recommendation systems. Retailers want to grow their business and increase sales. One embodiment displays recommendations for inside sales during calls to prospects via a CRM. Another embodiment improves genomic cross-sell by summing correlations between attributes. A third embodiment improves cross-channel personalization by linking personal information, preferably via a one-way hash, to a unique customer ID. A fourth embodiment enables a common core mobile app for different retailers. A fifth embodiment identifies a shopper before purchase to provide personal recommendations while shopping. A sixth embodiment utilizes a market place with shared customers for customer acquisition. A seventh embodiment utilizes customers' preferences and characteristics and sales data to influence recommendations. The characteristics can be combined into a shopper psychographic persona to generate recommendations.

Abstract: This invention deals with a computer-implemented method for using predictive analytics to find a second set of entries in a first array that are related to a first set of entries in the first array without directly calculating entry-to-entry similarities between entries in the first array, the method including: (a) identifying a target entry in a second array, wherein the first set of entries in the first array are linked to the target entry in the second array; (b) finding likely entries in the second array that have similarities to a target entry in the second array using a correlation; (c) finding linked entries in the first array that are linked to the likely entries found in the second array; and (d) using the linked entries found in the first array as the second set of entries in the first array.