Abstract:
Methods and arrangements are provided for the personalized targeting and customization of media stations in an online media service. A key set of inputs, with values unique to each user, is used to arrive at a personalized group of featured media stations that a user has access to. Demographic data, media preferences, user actions detected through the user's input, and other criteria allow for the content and screen time of featured stations to be personalized for each user.
Abstract:
Disclosed are systems, methods, and non-transitory computer-readable storage media for an online affiliate program for offline retail businesses. An online content retailer that sells content items (e.g., music, movies, books, etc.) can provide an affiliate program to offline retail businesses (e.g., brick and mortar businesses) whereby, upon entering into a contractual relationship to be an affiliate of the online content retailer, an offline retail business can receive a commission fee for business driven to the online content retailer by the offline retail business (e.g., purchases of content items from the online content retailer driven by the affiliate).
Abstract:
Techniques for preventing typographical errors on digital soft keyboards are implemented by a computing device with a touch-screen display. According to one technique, a plurality of soft keys of a soft keyboard is displayed on the touch-screen display. Each soft key covers an area of the touch-screen display. One or more occurrences of a particular typographical error in which a user erroneously selects an adjacent soft key in addition to or instead of an intended soft key are detected. In response, an activation region of the intended soft key is changed to decrease the probability of the user making the same typographical error in the future.
Abstract:
An online media station can be automatically generated based on a user's media preference data. Media preference data can include a user's media item purchase history. The media preference data is analyzed and media preference clusters are generated from the analyzed media preference data. Generated media preference clusters are ranked based on a predetermined set of ranking rules. The top ranked media preference clusters are selected dependent upon the user's number of slots available for customized media stations. One or more media station seeds are selected from each media preference cluster selected based on a set of predetermined selection rules. An algorithmic media station is automatically generated from the one or more media station seeds and provided to an electronic device of the user.
Abstract:
Methods and arrangements are provided to valuate advertising slots for a client device based on events that are detected on the device. The cost of the advertising slots can vary according to the timing of the user interactions or according to different classifications for the client device that are determined based on the events. The cost of the advertising slot can be determined before or after the invitational content is provided to the client device.
Abstract:
Disclosed herein are systems, methods, and non-transitory computer-readable storage media for increasing the performance of an advertisement network by monitoring requests for advertisements from applications, detecting patterns, and developing and implementing remedial actions to increase system performance.
Abstract:
Systems, methods, and computer-readable storage media for determining user engagement levels during a presentation of content. The system first collects data associated with a user session at a client device. Next, the system predicts a user engagement level during the user session by applying an engagement predicting rule to the data. The system can predicts respective user engagement levels for various segments of the presentation by applying one or more engagement predicting rules to the data. The system then presents invitational content based on the user engagement level.
Abstract:
Systems, methods, and computer-readable storage media for optimizing and reporting time spent by a user engaged in a session. The system first obtains data associated with a presentation of an item of content at a mobile device, the presentation being divided into multiple partitions. Based on the data, the system adjusts at least one respective length of time associated with the multiple partitions to yield at least one adjusted length of time, the at least one adjusted length of time reflecting estimated time of user engagement with content associated with at least one of the multiple partitions. The system then determines an amount of time spent by a user at the mobile device engaging in the presentation based on the at least one adjusted length of time and the multiple partitions.
Abstract:
Systems, methods, and computer-readable storage media for determining user engagement levels during a presentation of content. The system first collects data associated with a user session at a client device. Next, the system predicts a user engagement level during the user session by applying an engagement predicting rule to the data. The system can predicts respective user engagement levels for various segments of the presentation by applying one or more engagement predicting rules to the data. The system then presents invitational content based on the user engagement level.
Abstract:
Method and arrangements are provided to generate supplemental analysis modules for items of invitational content, the modules including an event dictionary and event handlers. Such modules are configured to collect event information at an end user device associated with the operation of an item of invitational content, determine whether the event information corresponds to an entry in the event dictionary, and, upon determining that the event information corresponds to an entry, identify an event handler corresponding to the entry and routing the event information to the event handler, where the event handler is configured for generating an event message.