Abstract: The disclosed embodiments relate to a computer implemented recommender system/method which enables the computer to provide more “emotionally” relevant/connected, and therefore more likely to be successful, recommendations while minimizing dependency on historical data as well susceptibility to bias, e.g., monetary and/or sponsor based. The disclosed embodiments segment a target set of known users into groups based on similar characteristics and then associate each group with one or more unique sets of affinities derived from the social media interactions of a general population having similar affinities. Similarly, a unique set of affinity characteristics is derived, and stored in a data structure, for each of a set of recommendations. The unique sets of affinities associated with the groups are compared with the set of affinity characteristics associated with each recommendations to derive a subset thereof relevant to the users of the particular group.

Abstract: The disclosed embodiments relate to a computer implemented recommender system/method which enables the computer to provide more “emotionally” relevant/connected, and therefore more likely to be successful, recommendations while minimizing dependency on historical data as well susceptibility to bias, e.g. monetary and/or sponsor based. The disclosed embodiments segment a target set of known users into groups based on similar characteristics and then associate each group with one or more unique sets of affinities derived from the social media interactions of a general population having similar affinities. Similarly, a unique set of affinity characteristics is derived, and stored in a data structure, for each of a set of recommendations. The unique sets of affinities associated with the groups are compared with the set of affinity characteristics associated with each recommendations to derive a subset thereof relevant to the users of the particular group.

Abstract: The disclosed embodiments relate to a computer implemented recommender system/method which enables the computer to provide more “emotionally” relevant/connected, and therefore more likely to be successful, recommendations while minimizing dependency on historical data as well susceptibility to bias, e.g. monetary and/or sponsor based. The disclosed embodiments segment a target set of known users into groups based on similar characteristics and then associate each group with one or more unique sets of affinities derived from the social media interactions of a general population having similar affinities. Similarly, a unique set of affinity characteristics is derived, and stored in a data structure, for each of a set of recommendations. The unique sets of affinities associated with the groups are compared with the set of affinity characteristics associated with each recommendations to derive a subset thereof relevant to the users of the particular group.

Abstract: Embodiments of the prevent invention allow a primary user to create a dependent account in order to control and monitor the transactions made by a dependent user. The primary user can limit the transactions that the dependent user can make at a store or for products. The primary user can set transaction amount limits and transaction time limits on the transactions the dependent user can make. The primary user may also set allocation limits that limit the amount of an allowed transaction that can be applied to the dependent account. The difference between the allowed transaction amount and the allocation limit amount is paid for through the use of another account. The alternate account may be pre-determined, may be determined at the time of the transaction, or at a later point in time after the transaction has been completed between the dependent user and the merchant.

Abstract: Embodiments of the prevent invention allow a primary user to create a dependent account in order to control and monitor the transactions made by a dependent user that is authorized to use the dependent account. The primary user can limit the transactions that the dependent user can make at a store or for products. The primary user can set transaction amount limits and transaction time limits on the transactions the dependent user can make at the blocked/approved stores or on the blocked/approved products. The primary user can set other limits, such as transaction type limits (e.g., in store purchase vs online purchase, or the like), location limits (e.g., radius location, zip code, state, region, or the like), or variance limits (e.g., particular transactions may be a percentage or dollar amount over the limits).