公开(公告)号：US20150074022A1

公开(公告)日：2015-03-12

申请号：US14070583

申请日：2013-11-04

申请人： concept.io, Inc.

发明人： Chris Cornelius ,  Dominic Hughes ,  Georgios Sofianatos ,  Gurumurthy D. Ramkumar ,  Max Delgadillo

IPC分类号： G06N7/00 ,  G06N99/00 ,  H04L29/08

CPC分类号： G06F17/30029 ,  G06F17/3053 ,  G06F17/30861 ,  G06N99/005 ,  H04L65/60 ,  H04L67/06 ,  H04L67/10 ,  H04N21/26258 ,  H04N21/43615 ,  H04N21/4825 ,  H04N21/4826 ,  H04N21/4828 ,  H04N21/6371

摘要： In one exemplary aspect, a sorted list of scored media content episodes is received with a computing device of a user. Each respective media content episode is scored by an iterative autotuning prediction algorithm, and wherein each element of the sorted list of scored media content episodes comprises a value that represents a likelihood of a user listening to the respective media content episode and a reference to a location of the respective media content episode. A number of bytes of a download iteration for each media content episode is determined based on value that represents a likelihood of the user listening to the respective media content episode and an index of the respective media content episode in the sorted list. It is detected that a mobile device is in the preferred network. The download iteration is implemented for each media content episode when it is detected that the mobile device is in the preferred network.

摘要翻译： 在一个示例性方面中，使用用户的计算设备接收得分的媒体内容剧集的排序列表。 通过迭代自动调谐预测算法对每个相应的媒体内容进行评分，并且其中得分的媒体内容剧集的排序列表的每个元素包括表示用户收听相应的媒体内容剧集和对某个位置的引用的可能性的值 各媒体内容集。 基于表示用户收听相应的媒体内容剧集的可能性的值和分类列表中各个媒体内容剧集的索引来确定每个媒体内容剧集的下载次数的多个字节。 检测到移动设备在优选网络中。 当检测到移动设备处于优选网络中时，为每个媒体内容插曲实现下载迭代。

公开(公告)号：US09495645B2

公开(公告)日：2016-11-15

申请号：US13954942

申请日：2013-07-30

申请人： concept.io, Inc.

发明人： Dominic Hughes ,  Gurumurthy D. Ramkumar ,  Georgios Sofianatos

IPC分类号： G06F15/18 ,  G06N99/00 ,  G06F17/30

CPC分类号： G06N99/005 ,  G06F17/30766

摘要： In one exemplary embodiment, a method of a computerized media-content recommender includes receiving a user-judgment score based on an historical user-listening data with respect to a media content. A first prediction score for a user with respect to the media content is calculated with a media-content recommender. The media-content recommender includes a first set of prediction parameters. A first prediction error including a difference between the user-judgment score and the first prediction score is determined. At least one parameter value of the first set of prediction parameters is modified with a machine-learning optimization technique to generate a second set of prediction parameters. A second prediction score for the user with respect to the media content is calculated with a media-content recommender. A second prediction error including a difference between the user-judgment score and the second prediction score is calculated.

摘要翻译： 在一个示例性实施例中，计算机化媒体内容推荐器的方法包括基于关于媒体内容的历史用户收听数据来接收用户判断得分。 使用媒体内容推荐器计算用户相对于媒体内容的第一预测分数。 媒体内容推荐器包括第一组预测参数。 确定包括用户判断分数与第一预测分数之间的差的第一预测误差。 通过机器学习优化技术修改第一组预测参数的至少一个参数值，以生成第二组预测参数。 使用媒体内容推荐器计算用户相对于媒体内容的第二预测分数。 计算包括用户判断分数和第二预测分数之间的差的第二预测误差。