Abstract: Systems and methods for providing recommendations to users while maintaining privacy and information security for those users. In particular, user demographic information and/or geographic/environmental information can be represented as hashes, or fingerprints, which in turn can define a dimension of a recommendation matrix having another dimension defined by attributes of products, services, routines, and so on that may be associated with recommendations to the user. The values of the recommendation matrix can correspond to normalized customer review data and/or other data.

Abstract: A system and method for reducing fragranced product industry waste by increasing accuracy of product recommendations for users, resulting in an decreasing a likelihood that fragranced products will be discarded prematurely. The system leverages a trained predictive model to determine true fragrance preferences and aversions. The predictive model is trained, at least in part, from user surveys intentionally de-emphasizing or entirely omitting industry vocabulary so as to elicit unbiased user responses.

Abstract: Systems and methods for generating user-specific recommendations of products or services by collaborative filtering executed and/or performed by one or more trained or untrained predictive models configured to ingest product attribute(s), product purpose(s), user location data, and/or user demographics. The predictive model(s) are leveraged to detect and determine user-specific preferences for, and preferences against, particular attributes, features, ingredients, aesthetic styles, and so on.

Abstract: Systems and methods for generating user-specific recommendations of products or services by collaborative filtering executed and/or performed by one or more trained or untrained predictive models configured to ingest product attribute(s), product purpose(s), user location data, and/or user demographics. The predictive model(s) are leveraged to detect and determine user-specific preferences for, and preferences against, particular attributes, features, ingredients, aesthetic styles, and so on.

Abstract: Systems and methods for operating a vending machine and one or more remote servers or services to generate user-specific recommendations of products or services by collaborative filtering executed and/or performed by one or more trained or untrained predictive models configured to ingest product attribute(s), product purpose(s), user location data, and/or user demographics. The predictive model(s) are leveraged to detect and determine user-specific preferences for, and preferences against, particular attributes, features, ingredients, aesthetic styles, and so on. Recommendations are leveraged to provide a custom selection of products and/or a custom mixture of products from the vending machine.

Abstract: Systems and methods for providing recommendations to users while maintaining privacy and information security for those users. In particular, user demographic information and/or geographic/environmental information can be represented as hashes, or fingerprints, which in turn can define a dimension of a recommendation matrix having another dimension defined by attributes of products, services, routines, and so on that may be associated with recommendations to the user. The values of the recommendation matrix can correspond to normalized customer review data and/or other data.

Abstract: A method of providing a skincare regimen recommendation and curated skincare product portfolio is described. A user may enter user skincare data and a user skin profile may be generated from this data. The user skin profile may be mapped to skin matrix factors of a user skin profile matrix to derive a skin profile identifier. The skin profile identifier may be used to select ingredients to formulate a curated product line, specific to the skin issues that the user identified. A recommendation for the curated product line may be provided to the user as well as the option for the user to purchase an individual curated product or the entire product line.