Content item recommendations based on content attribute sequence

    公开(公告)号:US09875245B2

    公开(公告)日:2018-01-23

    申请号:US14684063

    申请日:2015-04-10

    Applicant: Apple Inc.

    CPC classification number: G06F17/30053

    Abstract: User created playlists can be analyzed to create a statistical language model indicating the likelihood that a particular sequence of content attributes will be found in a playlist created by a user, as well as the likelihood of any sequence of one or more content attributes following a playlist or partial playlist created by a user. The language model can be used to generate a recommended content attribute sequence based on a partial playlist of one or more content items. A recommended content item sequence that will be pleasant to a user when added to the partial playlist can be selected based on the recommended content attribute sequence.

    STEERING FOR UNSTRUCTURED MEDIA STATIONS
    6.
    发明申请

    公开(公告)号:US20190079935A1

    公开(公告)日:2019-03-14

    申请号:US15885047

    申请日:2018-01-31

    Applicant: APPLE INC.

    Abstract: The present technology pertains to steering a playlisting service toward media items that are likely to receive positive feedback from a user operating a client device. The present technology permits a request to play media items without requiring an input context. A playlist service can begin to receive feedback on the playback of the media items and the received playback can be utilized by a steering service in response to a steering request to identify media items for playback that are likely to receive positive feedback based on the feedback received on a sequence of previously played media items.

    Content Item Recommendations Based On Content Attribute Sequence
    8.
    发明申请
    Content Item Recommendations Based On Content Attribute Sequence 有权
    基于内容属性序列的内容项目建议

    公开(公告)号:US20160299906A1

    公开(公告)日:2016-10-13

    申请号:US14684063

    申请日:2015-04-10

    Applicant: Apple Inc.

    CPC classification number: G06F17/30053

    Abstract: User created playlists can be analyzed to create a statistical language model indicating the likelihood that a particular sequence of content attributes will be found in a playlist created by a user, as well as the likelihood of any sequence of one or more content attributes following a playlist or partial playlist created by a user. The language model can be used to generate a recommended content attribute sequence based on a partial playlist of one or more content items. A recommended content item sequence that will be pleasant to a user when added to the partial playlist can be selected based on the recommended content attribute sequence.

    Abstract translation: 可以分析用户创建的播放列表以创建统计语言模型,指示在用户创建的播放列表中将找到特定的内容属性序列的可能性,以及播放列表之后的一个或多个内容属性的任何序列的可能性 或由用户创建的部分播放列表。 语言模型可以用于基于一个或多个内容项的部分播放列表生成推荐的内容属性序列。 可以基于推荐的内容属性序列来选择在添加到部分播放列表时对于用户而言愉快的推荐内容项序列。

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