Systems and methods for objective-based scoring using machine learning techniques

    公开(公告)号:US10419440B2

    公开(公告)日:2019-09-17

    申请号:US16272761

    申请日:2019-02-11

    摘要: Certain aspects and features of the present disclosure relate to systems and methods that generate machine-learning models to predict whether user devices are likely to meet defined objectives. For example, a machine-learning model can be generated to predict whether or not a user device is likely to access a resource. In some implementations, a semi-supervised model can be used to determine to what extent user devices are predicted to satisfy the defined objective(s). For example, a resource-affinity parameter can be generated as a result of inputting various data points into a semi-supervised model. The various data points can be access from a plurality of data sources, and can represent one or more activities or attributes associated with a user. The value of the resource-affinity parameter can be evaluated to determine the extent to which the user is likely to meet an objective.

    Systems and methods for objective-based scoring using machine learning techniques

    公开(公告)号:US10205728B2

    公开(公告)日:2019-02-12

    申请号:US15983475

    申请日:2018-05-18

    摘要: Certain aspects and features of the present disclosure relate to systems and methods that generate machine-learning models to predict whether user devices are likely to meet defined objectives. For example, a machine-learning model can be generated to predict whether or not a user device is likely to access a resource. In some implementations, a semi-supervised model can be used to determine to what extent user devices are predicted to satisfy the defined objective(s). For example, a resource-affinity parameter can be generated as a result of inputting various data points into a semi-supervised model. The various data points can be access from a plurality of data sources, and can represent one or more activities or attributes associated with a user. The value of the resource-affinity parameter can be evaluated to determine the extent to which the user is likely to meet an objective.

    Biased ticket offers for actors identified using dynamic assessments of actors' attributes

    公开(公告)号:US12073339B2

    公开(公告)日:2024-08-27

    申请号:US18319106

    申请日:2023-05-17

    IPC分类号: G06Q10/02 G06Q30/02 H04L9/40

    摘要: Techniques herein attempt to provide actors with more flexible and satisfactory experiences regarding obtaining tickets for an event. A learning model may identify attributes indicative of whether a particular actor (e.g., attempting to purchase tickets to an event) possesses a desirable characteristic (e.g., is likely to attend the event). Each actor can then be evaluated to estimate whether she is a good actor (possesses the characteristic). If so, favored opportunities may be made available, such as the opportunity to buy high-demand tickets. An actor may further have the opportunity to hold or reserve tickets for a period time, during which other actors cannot purchase them. A fee for holding or reserving tickets (and/or maintaining the hold or reserve) can be dynamically set based on market factors. Opportunities to modify seat assignments to allow a group of friends to sit together may also be provided.