Abstract: Methods and systems for content optimization are described. A computing device may determine a predictability score that indicates a probability that a device will access a first content item. The computing device may send a second content item associated with the first content item. The second content item may be based on the predictability score, and the predictability score may be modified. Additional content consumption and/or recommendations may be adjusted based on the predictability score.

Abstract: Methods, systems, and apparatuses for determining viewership of a content item are described herein. Machine learning techniques may be used to determine which user(s) among a user group at a multi-user location is consuming a content item. A machine learning model may be trained using demographic attributes and content attributes associated with a plurality of single-user locations. A probability engine may train a machine learning model using the demographic attributes and content attributes and one or more machine learning algorithms. The trained machine learning model may be used to determine which user(s) among at least two users is consuming a content item at a multi-user location at which multiple people reside.

Abstract: A time associated with last selection of a content item at a user device may be determined. A time associated with a last user interaction with the user device may be determined. During presentation of the content item at the user device, it may be determined that a supplemental content insertion opportunity is available. A request for a supplemental content item may be sent to a server, wherein the request comprises data indicating the time associated with the last selection of the content item at the user device and data indicating the time associated with the last user interaction with the user device.

Abstract: Systems, methods, and devices relating to determining viewership data are described herein. In a method, viewing data associated with a household is received. A first portion of the viewing data is indicative of video programming associated with a first video device and a second portion of the viewing data is indicative of video programming associated with a second video device. One or more characteristics associated with the first and second portions of the viewing data are determined. Based on the one or more characteristics and a comparison of the respective video programming associated with the first and second video devices, it is determined that the first portion of the viewing data is duplicative, at least in part, with the second portion of the viewing data.

Abstract: Methods and systems for content optimization are described. A computing device may determine a predictability score that indicates a probability that a device will access a first content item. The computing device may send a second content item associated with the first content item. The second content item may be based on the predictability score, and the predictability score may be modified. Additional content consumption and/or recommendations may be adjusted based on the predictability score.

Abstract: Systems, methods, and devices relating to determining viewership data are described herein. In a method, viewing data associated with a household is received. A first portion of the viewing data is indicative of video programming associated with a first video device and a second portion of the viewing data is indicative of video programming associated with a second video device. One or more characteristics associated with the first and second portions of the viewing data are determined. Based on the one or more characteristics and a comparison of the respective video programming associated with the first and second video devices, it is determined that the first portion of the viewing data is duplicative, at least in part, with the second portion of the viewing data.

Abstract: Methods, systems, and apparatuses for determining viewership of a content item are described herein. Machine learning techniques may be used to determine which user(s) among a user group at a multi-user location is consuming a content item. A machine learning model may be trained using demographic attributes and content attributes associated with a plurality of single-user locations. A probability engine may train a machine learning model using the demographic attributes and content attributes and one or more machine learning algorithms. The trained machine learning model may be used to determine which user(s) among at least two users is consuming a content item at a multi-user location at which multiple people reside.

Abstract: A time associated with last selection of a content item at a user device may be determined. A time associated with a last user interaction with the user device may be determined. During presentation of the content item at the user device, it may be determined that a supplemental content insertion opportunity is available. A request for a supplemental content item may be sent to a server, wherein the request comprises data indicating the time associated with the last selection of the content item at the user device and data indicating the time associated with the last user interaction with the user device.

Abstract: Methods and systems for content optimization are described. A computing device may determine a predictability score that indicates a probability that a device will access a first content item. The computing device may send a second content item associated with the first content item. The second content item may be based on the predictability score, and the predictability score may be modified. Additional content consumption and/or recommendations may be adjusted based on the predictability score.

Abstract: Methods and systems for content optimization are described. A computing device may determine a predictability score that indicates a probability that a device will access a first content item. The computing device may send a second content item associated with the first content item. The second content item may be based on the predictability score, and the predictability score may be modified. Additional content consumption and/or recommendations may be adjusted based on the predictability score.

Abstract: Methods, systems, and apparatuses for determining viewership of a content item are described herein. Machine learning techniques may be used to determine which user(s) among a user group at a multi-user location is consuming a content item. A machine learning model may be trained using demographic attributes and content attributes associated with a plurality of single-user locations. A probability engine may train a machine learning model using the demographic attributes and content attributes and one or more machine learning algorithms. The trained machine learning model may be used to determine which user(s) among at least two users is consuming a content item at a multi-user location at which multiple people reside.

Abstract: Methods and systems for content optimization are described. A computing device may determine a predictability score that indicates a probability that a device will access a first content item. The computing device may send a second content item associated with the first content item. The second content item may be based on the predictability score, and the predictability score may be modified. Additional content consumption and/or recommendations may be adjusted based on the predictability score.

Abstract: Systems, methods, and devices relating to determining viewership data are described herein. In a method, viewing data associated with a household is received. A first portion of the viewing data is indicative of video programming associated with a first video device and a second portion of the viewing data is indicative of video programming associated with a second video device. One or more characteristics associated with the first and second portions of the viewing data are determined. Based on the one or more characteristics and a comparison of the respective video programming associated with the first and second video devices, it is determined that the first portion of the viewing data is duplicative, at least in part, with the second portion of the viewing data.