Abstract: Methods, systems, and apparatuses for content recommendations based on user activity data are described herein. An analytics subsystem may receive first activity data. The first activity may be indicative of a plurality of first engagements with a first plurality of media assets. At least one machine learning model may be configured to receive an input of activity data, such as the first activity data, and to determine at least one content recommendation on that basis. The at least one content recommendation may comprise a recommendation for at least one media asset. The at least one media asset may be associated with at least one media asset classification. The analytics subsystem may send the at least one content recommendation.

Abstract: Methods, systems, and apparatuses for content recommendations based on user activity data are described herein. An analytics subsystem may receive first activity data. The first activity may be indicative of a plurality of first engagements with a first plurality of media assets. At least one machine learning model may be configured to receive an input of activity data, such as the first activity data, and to determine at least one content recommendation on that basis. The at least one content recommendation may comprise a recommendation for at least one media asset. The at least one media asset may be associated with at least one media asset classification. The analytics subsystem may send the at least one content recommendation.

Abstract: Methods, systems, and apparatuses for improved content recommendations are described herein. A distribution platform may comprise a system of computing devices, servers, software, etc., that is configured to present media assets (e.g., content) at user devices. In one example embodiment, an analytics subsystem may provide at least one content recommendation to a user device using a classification model. In another example embodiment, the analytics subsystem may train the classification model. In a further example embodiment, the analytics subsystem may provide at least one fallback content recommendation when a recommendation provided by the classification model does not satisfy a threshold level of interest.

Abstract: Methods, systems, and apparatuses for user engagement analysis are described herein. An analytics subsystem may use a plurality of activity data to generate a plurality of user profiles, corresponding user interest clouds for each user device of a plurality of user devices, and a first interest cloud associated with a particular client identifier. The analytics subsystem may generate a second interest cloud associated based on a subset of a plurality of media assets associated with a threshold quantity of engagements. The analytics subsystem may determine a plurality of clusters of the plurality of media assets and may generate a content interest cloud for each of the plurality of clusters.

Abstract: Methods, systems, and apparatuses for improved model selection and content recommendations are described herein. A distribution platform may comprise a system of computing devices, servers, software, etc., that is configured to present media assets (e.g., content) at user devices. In one example embodiment, an analytics subsystem may provide at least one content recommendation to a user device using a machine learning model. The machine learning model may be selected based on a clustering method using an unsupervised machine learning model.

Abstract: Methods, systems, and apparatuses for user segmentation and analysis are described herein. An analytics subsystem may use a plurality of activity data to generate a plurality of user profiles, corresponding user interest clouds for each user device of a plurality of user devices, and a geographic interest cloud associated with a particular client identifier. In another example embodiment, the analytics subsystem may generate an industry interest cloud associated with the particular client identifier. For example, the industry interest cloud may indicate interests of users associated with a particular industry. In a further example embodiment, the analytics subsystem may determine (e.g., identify) a plurality of clusters of users. For example, each of the plurality of clusters of users may comprise one or more user profiles having common interests, common geographic location, common industry affiliation, a combination thereof, and/or the like.

Abstract: A gantry system for processing an aircraft includes a gantry receiving a fuselage of the aircraft having a gantry frame with an arched member. The gantry system includes first and second locating arms extending between inner and outer ends movably coupled to the gantry frame. The gantry system includes a bracket assembly having a first locating arm bracket pivotably coupled to the first locating arm, a second locating arm bracket pivotably coupled to the second locating arm, and a head assembly bracket with a head assembly coupled thereto having an end effector for processing the fuselage. The head assembly is variably positionable by the first and second locating arms moving along the gantry frame for positioning the end effector relative to the fuselage.

Abstract: A gantry system for processing an aircraft includes a gantry receiving a fuselage of the aircraft having a gantry frame with an arched member. The gantry system includes first and second locating arms extending between inner and outer ends movably coupled to the gantry frame. The gantry system includes a bracket assembly having a first locating arm bracket pivotably coupled to the first locating arm, a second locating arm bracket pivotably coupled to the second locating arm, and a head assembly bracket with a head assembly coupled thereto having an end effector for processing the fuselage. The head assembly is variably positionable by the first and second locating arms moving along the gantry frame for positioning the end effector relative to the fuselage.