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    • 5. 发明授权
    • User behavior model for contextual personalized recommendation
    • 用户行为模型的上下文个性化推荐
    • US08751472B2
    • 2014-06-10
    • US13111132
    • 2011-05-19
    • Tao MeiYing-Qing XuShipeng LiJinfeng ZhuangBo ZhangPeng Xu
    • Tao MeiYing-Qing XuShipeng LiJinfeng ZhuangBo ZhangPeng Xu
    • G06F17/30
    • G06F17/3053G06F17/30554G06Q30/02
    • A user behavior model provides personalized recommendations based in part on time and location, particularly to users of mobile devices. Entity types are ranked according to relevance to the user. Example entity types are restaurant, hotel, etc. The relevance may be based on reference to a large-scale database containing queries from other users. Additionally, entities within each entity type may be ranked based on relevance to the user and the time and location context. A user interface may display a ranked list of entity types, such as restaurant, hotel, etc., wherein each entity type is represented by a highest-ranked entity with the entity type. Thus, the user interface may display a highest-ranked restaurant, a highest-ranked hotel, etc. Upon user selection of one such entity type the user interface is replaced with a second user interface, for example showing a ranked hierarchy of restaurants, headed by the highest-ranked restaurant.
    • 用户行为模型部分地基于时间和位置提供个性化建议,特别是移动设备的用户。 实体类型根据与用户的相关性进行排名。 示例实体类型是餐厅,酒店等。相关性可以基于参考包含来自其他用户的查询的大规模数据库。 另外,每个实体类型中的实体可以基于与用户的相关性以及时间和位置上下文来排序。 用户界面可以显示诸如餐馆,酒店等的实体类型的排名列表,其中每个实体类型由具有实体类型的最高排名的实体表示。 因此,用户界面可以显示排名最高的餐馆,排名最高的酒店等。当用户选择一个这样的实体时,类型将用户界面替换为第二用户界面,例如显示餐馆的等级排列 由最高排名的餐厅。
    • 6. 发明申请
    • Recommendations for Social Network Based on Low-Rank Matrix Recovery
    • 基于低阶矩阵恢复的社会网络建议
    • US20120297038A1
    • 2012-11-22
    • US13108843
    • 2011-05-16
    • Tao MeiXian-Sheng HuaShipeng LiJinfeng Zhuang
    • Tao MeiXian-Sheng HuaShipeng LiJinfeng Zhuang
    • G06F15/173G06F17/30
    • G06Q50/01
    • Techniques describe analyzing users and groups of a social network to identify user interests and providing recommendations for a user based on the user's identified interests. A content-awareness application obtains a collection of images and tags associated with the images belonging to members in the social network. The content-awareness application decomposes the members into a representative matrix to identify users and groups in order to calculate a similarity matrix between the users and their images based on a visual content of the images and a textual content of the tags. The content-awareness application further constructs a graph Laplacian over the users and the groups to align with the representative matrix based at least in part on the similarity matrix and further provides recommendations of groups for a user to join in the social network based at least in part on the graph Laplacian identifying the user's interests.
    • 技术描述了分析社交网络的用户和组以识别用户兴趣并基于用户识别的兴趣为用户提供建议。 内容感知应用程序获得与属于社交网络中的成员的图像相关联的图像和标签的集合。 内容感知应用程序将成员分解为代表性矩阵以识别用户和组,以便基于图像的可视内容和标签的文本内容来计算用户和他们的图像之间的相似性矩阵。 该内容感知应用进一步构建一个关于用户和组的拉普拉斯算子,以至少部分地基于相似性矩阵与代表性矩阵一致,并进一步提供用户群体的建议,以便用户至少在 拉普拉斯确定用户兴趣的一部分。
    • 7. 发明申请
    • User Behavior Model for Contextual Personalized Recommendation
    • 上下文个性化推荐的用户行为模型
    • US20120295640A1
    • 2012-11-22
    • US13111132
    • 2011-05-19
    • Tao MeiYing-Qing XuShipeng LiJinfeng ZhuangBo ZhangPeng Xu
    • Tao MeiYing-Qing XuShipeng LiJinfeng ZhuangBo ZhangPeng Xu
    • H04W64/00
    • G06F17/3053G06F17/30554G06Q30/02
    • A user behavior model provides personalized recommendations based in part on time and location, particularly to users of mobile devices. Entity types are ranked according to relevance to the user. Example entity types are restaurant, hotel, etc. The relevance may be based on reference to a large-scale database containing queries from other users. Additionally, entities within each entity type may be ranked based on relevance to the user and the time and location context. A user interface may display a ranked list of entity types, such as restaurant, hotel, etc., wherein each entity type is represented by a highest-ranked entity with the entity type. Thus, the user interface may display a highest-ranked restaurant, a highest-ranked hotel, etc. Upon user selection of one such entity type the user interface is replaced with a second user interface, for example showing a ranked hierarchy of restaurants, headed by the highest-ranked restaurant.
    • 用户行为模型部分地基于时间和位置提供个性化建议,特别是移动设备的用户。 实体类型根据与用户的相关性进行排名。 示例实体类型是餐厅,酒店等。相关性可以基于参考包含来自其他用户的查询的大规模数据库。 另外,每个实体类型中的实体可以基于与用户的相关性以及时间和位置上下文来排序。 用户界面可以显示诸如餐馆,酒店等的实体类型的排名列表,其中每个实体类型由具有实体类型的最高排名的实体表示。 因此,用户界面可以显示排名最高的餐馆,排名最高的酒店等。当用户选择一个这样的实体时,类型将用户界面替换为第二用户界面,例如显示餐馆的等级排列 由最高排名的餐厅。