The Small Business Innovation Research (SBIR) Phase I project addresses the opportunity associated with the explosion in the number of goods/services available for consumption. The proposed technology is one that, if successful, will predict which goods/services would optimally satisfy individual consumers by innovatively identifying the why behind users' preferences, to save time, effort and money for consumers on their web-enabled smartphones. The approach utilizes semantic analysis to evaluate millions of online reviews, identifying why populations in general like/dislike specific entertainment opportunities such as restaurants, live events, clubs, or shows. It then begins the creation of a rich and nuanced User profile utilizing semantic analysis of user opinions, implicit or explicit feedback on previous feedback and their characteristics, and behavioral tracking. As the technology makes predictions, feedback mechanisms including free text by the user continually improve understanding of the User and improve the performance of the recommendations. Predictions are made contextually relevant, incorporating behavioral and contextual information such as GPS location data, weather conditions, recent search queries, and implicit indications of interest mined from the user's social network profiles and interactions. The Small Business Innovation Research (SBIR) Phase I project addresses the opportunity associated with the explosion in the number of goods/services available for consumption. The proposed technology is one that, if successful, will predict which goods/services would optimally satisfy individual consumers by innovatively identifying the why behind users' preferences, to save time, effort and money for consumers on their web-enabled smartphones. The approach utilizes semantic analysis to evaluate millions of online reviews, identifying why populations in general like/dislike specific entertainment opportunities such as restaurants, live events, clubs, or shows. It then begins the creation of a rich and nuanced User profile utilizing semantic analysis of user opinions, implicit or explicit feedback on previous feedback and their characteristics, and behavioral tracking. As the technology makes predictions, feedback mechanisms including free text by the user continually improve understanding of the User and improve the performance of the recommendations. Predictions are made contextually relevant, incorporating behavioral and contextual information such as GPS location data, weather conditions, recent search queries, and implicit indications of interest mined from the user's social network profiles and interactions. The approach seeks to offer "brilliant predictions that surprise and delight" its users, by answering the question, "What restaurant, bar, club or live event would you love" For advertisers, it provides previously unseen levels of customer segmentation, advertising only to interested individuals. Recommendations of goods/services sponsored by the advertiser are delivered via internet and GPS enabled smart phones. Through special promotions (such as coupons and discounts), the mobile application tracks consumer shopping behavior at the physical location of the Advertiser. This generates simple robust conversion metrics for the advertiser, tracking actual sales and directly elucidating advertising cost efficiency. Heretofore, proof of direct causal linkage between advertising and an actual customer sale/new customer lead was primarily limited to the internet, where banner ad click through to an online purchase could be tracked. The proposed approach brings innovative sales performance based advertising and online efficiency to physical purchases in the real world and if successful will address a significant and growing opportunity
