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    • 4. 发明申请
    • STRENGTH BASED MODELING FOR RECOMMENDATION SYSTEM
    • 基于强度建模的建模系统
    • WO2015038444A3
    • 2015-06-11
    • PCT/US2014054451
    • 2014-09-08
    • MICROSOFT CORP
    • NICE NIRKOENIGSTEIN NOAMPAQUET ULRICHKEREN SHAHARSITTON DANIEL
    • G06Q30/06
    • G06Q30/0631
    • Example apparatus and methods provide a recommendation to a user about a product they may wish to consider purchasing. One method produces a single indication concerning a relationship between a user and an item with which the user has interacted. The single indication identifies whether the user likes the item and the degree to which the user likes the item. The single indication is independent of user signals processed to compute the single indication. The single indication is produced by a signal deriver that is loosely coupled to a model of users and items. The model may be a matrix upon which matrix factorization can be performed. Although matrix factorization is performed, it is performed on vectors whose elements are independent of the signals processed by the signal deriver. Since users may have different preferences at different times, the degree to which the user likes the item may be manipulated.
    • 示例性设备和方法向用户提供关于他们可能希望考虑购买的产品的推荐。 一种方法产生关于用户与用户与之交互的项目之间的关系的单一指示。 单个指示标识用户是否喜欢该项目以及用户喜欢该项目的程度。 单个指示与被处理以计算单个指示的用户信号无关。 单个指示由松散耦合到用户和项目模型的信号提取器产生。 该模型可以是可以执行矩阵分解的矩阵。 虽然执行了矩阵分解,但是对其元素独立于由信号提升器处理的信号的矢量执行。 由于用户可能在不同时间具有不同的偏好,所以可以操纵用户喜欢该项目的程度。
    • 5. 发明申请
    • A PRIVACY VAULT FOR MAINTAINING THE PRIVACY OF USER PROFILES
    • 用于维护用户配置文件隐私的隐私保护
    • WO2011044232A3
    • 2011-06-30
    • PCT/US2010051625
    • 2010-10-06
    • MICROSOFT CORP
    • NICE NIRDUNN MELISSA WPICARD ERICSHAKED AMITVAN VALKENBURG ERIC DONGOUNARES ALEXANDER GEORGEARIE FRIEDMANOPHIR SEFYFELDBAUM BOAZHA VU ACANNON DARRELL JAYTOUTONGHI MICHAEL JOSEPHBARASH URIDWORK CYNTHIAMAH TERESALI YING
    • G06Q50/00
    • G06Q30/02G06F21/6263
    • Methods, systems, and computer-readable media for facilitating personalization of web content is provided, while protecting privacy of the user data utilized to personalize user's experience. A privacy vault may collect user data including user activity data, demographic data, and user interests submitted by user. In one embodiment, the privacy vault operates on a user client device. The privacy vault sends user data to a community vault that collects user data from multiple users. The community vault generates segment rules that whether user belongs to a user segment, which expresses user's interest. The segment rules are then communicated back to privacy vault, which assigns one or more user segments to the user based on user data available to the privacy vault and the segment rules. The privacy vault may communicate user segments to one or more content providers that supply personalized content that is selected based on the user segments provided.
    • 提供了用于促进网页内容的个性化的方法,系统和计算机可读介质,同时保护用于个性化用户体验的用户数据的隐私。 隐私保险库可以收集用户数据,包括用户活动数据,人口统计数据和用户提交的用户兴趣。 在一个实施例中,隐私保险库在用户客户端设备上运行。 隐私保护库将用户数据发送到从多个用户收集用户数据的社区保管库。 社区保管库会生成段规则,无论用户是否属于用户区段,表示用户的兴趣。 分段规则然后被传送回隐私保护库,隐私保险库基于隐私保险库和段规则可用的用户数据向用户分配一个或多个用户段。 隐私保险库可以将用户段传达给提供基于所提供的用户段选择的个性化内容的一个或多个内容提供者。
    • 7. 发明申请
    • FEATURE EMBEDDING IN MATRIX FACTORIZATION
    • 矩阵分解的特征嵌入
    • WO2014100321A2
    • 2014-06-26
    • PCT/US2013076362
    • 2013-12-19
    • MICROSOFT CORP
    • NICE NIRKOENIGSTEIN NOAMPAQUET ULRICHKEREN SHAHAR ZVIJAFFRAY ANDREW
    • G06Q30/02
    • G06F17/16
    • In various embodiments, systems and methods are provided for enhancing media content recommendations by using feature vectors. An enhanced-matrix having a first portion and a second portion is received. The first portion of the enhanced-matrix includes a user-item matrix and the second portion of the enhanced-matrix includes a feature-item matrix. Each entry in the feature-item matrix is item metadata. An item-stem vector is determined based on a weighted sum of each of the feature vectors associated with the item. An item-latent-trait vector is generated based on the item-stem vector and an item-offset vector. The item-offset vector is an item vector for the item in the user-item matrix. One or more recommended-media content derived based on the item-latent-trait vector is provided.
    • 在各种实施例中,提供了通过使用特征向量来增强媒体内容推荐的系统和方法。 接收具有第一部分和第二部分的增强矩阵。 增强矩阵的第一部分包括用户项矩阵,并且增强矩阵的第二部分包括特征项矩阵。 特征项矩阵中的每个条目都是项目元数据。 项目干矢量是基于与项目相关联的每个特征矢量的加权和来确定的。 项目潜在特征向量是基于项目干向量和项目偏移向量生成的。 项目偏移矢量是用户项目矩阵中项目的项目矢量。 提供基于项目潜在性状矢量导出的一个或多个推荐媒体内容。