Abstract: Systems and methods for recommending an item from a plurality of items. The plurality of items includes chemistry-based features. The system comprises a computing device and a database. The database includes user preference data, item data, and mapping data. The system receives user input regarding user preferences, and associates user preference values with chemistry-based feature values. The system compares chemistry-based feature values for each item to the user preference values and returns items that best match. In one embodiment, the items are wines and the system uses chemistry-based features of wines such as wine volatile chemical compounds to return wine recommendations.

Abstract: Systems and methods for recommending an item from a plurality of items. The plurality of items includes chemistry-based features. The system comprises a computing device and a database. The database includes user preference data, item data, and mapping data. The system receives user input regarding user preferences, and associates user preference values with chemistry-based feature values. The system compares chemistry-based feature values for each item to the user preference values and returns items that best match. In one embodiment, the items are wines and the system uses chemistry-based features of wines such as wine volatile chemical compounds to return wine recommendations.

Abstract: Systems and methods for recommending an item from a plurality of items. The plurality of items includes chemistry-based features. The system comprises a computing device and a database. The database includes user preference data, item data, and mapping data. The system receives user input regarding user preferences, and associates user preference values with chemistry-based feature values. The system compares chemistry-based feature values for each item to the user preference values and returns items that best match. In one embodiment, the items are wines and the system uses chemistry-based features of wines such as wine volatile chemical compounds to return wine recommendations.

Abstract: Systems and methods for recommending an item from a plurality of items. The plurality of items includes chemistry-based features. The system comprises a computing device and a database. The database includes user preference data, item data, and mapping data. The system receives user input regarding user preferences, and associates user preference values with chemistry-based feature values. The system compares chemistry-based feature values for each item to the user preference values and returns items that best match. In one embodiment, the items are wines and the system uses chemistry-based features of wines such as wine volatile chemical compounds to return wine recommendations.

Abstract: A system, method and software application for neuropsychological evaluation screening, the system comprising: an application server controlling a software application installed in a communication device, the server comprising at least one processor configured to execute code instructions for: generating a classifier engine based on communication data and diagnosis of diagnosed subjects by input feature signals of the communication data to a plurality of classifiers, calculate predictive accuracy for each classifier, and generate a combination of classifiers based on the predictive accuracy; collecting text and vocal data from multiple communication channels at the communication device; input feature signals of the collected data to the plurality of classifiers; and execute the combination of classifiers.

Abstract: Systems and methods for recommending an item from a plurality of items. The plurality of items includes chemistry-based features. The system comprises a computing device and a database. The database includes user preference data, item data, and mapping data. The system receives user input regarding user preferences, and associates user preference values with chemistry-based feature values. The system compares chemistry-based feature values for each item to the user preference values and returns items that best match. In one embodiment, the items are wines and the system uses chemistry-based features of wines such as wine volatile chemical compounds to return wine recommendations.

Abstract: A computerized recommender system and an associated computer-implemented method, which accumulate and analyze the information about various marketing actions for promoting brands as well as information about the subsequent brand performance within media channels. The channels include, but are not limited to web, blogs, social networks, search engines, contextual ad networks, email campaigns, and other promotional activities. Additional media channels can be added into the recommender system. The described recommender system enables the marketers to obtain custom advice for a given brand as to what should be the next most effective marketing actions to promote the brand.