Methods, apparatus, and systems to collect audience measurement data are disclosed. Disclosed example systems include first means for processing to collect (i) first media identification data corresponding to a first time period and (ii) second media identification data corresponding to a second time period after the first time period, the first media identification data and the second media identification data associated with a monitored location and second means for processing to develop behavior data based on the first media identification data and people meter data collected during the first time period with a people meter associated with the monitored location, the people meter to be deactivated during the second time period and identify an audience member associated with the second media identification data based on the behavior data and without access to any people meter data corresponding to the second time period.

Methods, apparatus, and systems to collect audience measurement data are disclosed. Disclosed example systems include first means for processing to collect (i) first media identification data corresponding to a first time period and (ii) second media identification data corresponding to a second time period after the first time period, the first media identification data and the second media identification data associated with a monitored location and second means for processing to develop behavior data based on the first media identification data and people meter data collected during the first time period with a people meter associated with the monitored location, the people meter to be deactivated during the second time period and identify an audience member associated with the second media identification data based on the behavior data and without access to any people meter data corresponding to the second time period.

Systems and methods are presented for providing personalized content based on gestures, in order to provide a seamless and user-driven feedback capture platform that captures user gestures and provides improved personalized content. The system captures biometric data from the user during the presentation of a first content item from a first content source. The system identifies the feedback indicator from the biometric data captured during the output of the first content item and stores an identifier of the second content item associated with the first content item in the master list. The system generates the master list for display and, in response to receiving a selection of the identifier of the second content item, retrieves for output the second content item from the second content source on the user device.

Aspects of the subject disclosure may include, for example, a method that obtains consumption information for each of a group of communication devices associated with a user. A data plan is identified that is applicable to a communication device of the group of communication devices, and a group of media content is identified for the user based on the consumption information. The media content is provided to the communication device at a bit rate that varies according to the data plan. Other embodiments are disclosed.

Aspects of the subject disclosure may include, for example, a method that obtains consumption information for each of a group of communication devices associated with a user. A data plan is identified that is applicable to a communication device of the group of communication devices, and a group of media content is identified for the user based on the consumption information. The media content is provided to the communication device at a bit rate that varies according to the data plan. Other embodiments are disclosed.

Media content search results ranked by popularity is described. In embodiment(s), a search request for television media content can be initiated by a viewer, and television media content that is relevant to the search request can be identified. The relevant television media content can then be ranked based on a popularity rating and the relevant television media content can be displayed in an ordered list that is ordered by popularity rankings.

Media content search results ranked by popularity is described. In embodiment(s), a search request for television media content can be initiated by a viewer, and television media content that is relevant to the search request can be identified. The relevant television media content can then be ranked based on a popularity rating and the relevant television media content can be displayed in an ordered list that is ordered by popularity rankings.

Methods and apparatus to perform an automated gain control protocol with an amplifier based on historical data corresponding to contextual data are disclosed. Example apparatus disclosed herein are to select an automatic gain control (AGC) parameter for an AGC protocol based on historical data corresponding to contextual data, the contextual data including at least one of a time during which the AGC protocol is performed, a panelist identified by a meter, demographics of an audience identified by the meter, a location of the meter, a station identified by the meter, a media type identified by the meter, or a sound pressure level identified by the meter. The disclosed example apparatus are also to perform the AGC protocol based on the selected AGC parameter.

A consumption probability metric may be generated for a media asset. An aggregated forecast predicting user consumption of a media asset is received. A plurality of probabilities, each corresponding to a user of a plurality of users, is received, each indicating how likely a respective user is to consume the media asset. A weight for the plurality of users is calculated representing a ratio of the total number of users to a number of users in the plurality of users. A disaggregated forecast predicting user consumption of a media asset is determined based on the weight for the plurality of users and the plurality of probabilities. A modification factor is computed based on the aggregated forecast and the disaggregated forecast. A metric is generated that includes a plurality of user identifiers associated with the plurality of users and a plurality of modified probabilities each modified by the modification factor.

A consumption probability metric may be generated for a media asset. An aggregated forecast predicting user consumption of a media asset is received. A plurality of probabilities, each corresponding to a user of a plurality of users, is received, each indicating how likely a respective user is to consume the media asset. A weight for the plurality of users is calculated representing a ratio of the total number of users to a number of users in the plurality of users. A disaggregated forecast predicting user consumption of a media asset is determined based on the weight for the plurality of users and the plurality of probabilities. A modification factor is computed based on the aggregated forecast and the disaggregated forecast. A metric is generated that includes a plurality of user identifiers associated with the plurality of users and a plurality of modified probabilities each modified by the modification factor.