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1. 发明申请

WO2015153240A1 DIRECTED RECOMMENDATIONS 审中-公开
标题翻译：指导性建议
公开(公告)号：WO2015153240A1
公开(公告)日：2015-10-08
申请号：PCT/US2015/022602
申请日：2015-03-26
申请人： MICROSOFT TECHNOLOGY LICENSING, LLC
发明人： NICE, Nir , KOENIGSTEIN, Noam , PAQUET, Ulrich , FINKELSTEIN, Yehuda
IPC分类号： G06Q30/06
CPC分类号： G06Q30/0631 , G06Q30/02
摘要： Example apparatus and methods perform matrix factorization (MF) on a usage matrix to create a latent space that describes similarities between items in the usage matrix. The usage matrix relates source items that a user already has to target items that a user might acquire. A cell in the usage matrix may store a value that describes the likelihood (e.g., probability) that an acquisition of item x will lead to an acquisition of item y. The value stored in cell (x,y) is not transitive with the value stored in cell (y,x). Values that are missing in the usage matrix may be computed using vectors in the latent space. Once the usage matrix is updated, a directed recommendation may be produced from data in the usage matrix. Initial values in the usage matrix may be produced from data associated with actual acquisitions.
摘要翻译：示例性设备和方法在使用矩阵上执行矩阵分解（MF）以创建描述使用矩阵中的项之间的相似性的潜在空间。使用矩阵涉及用户已经必须定位用户可能获取的项目的源项目。使用矩阵中的单元可以存储描述项目x的获取将导致项目y的获取的可能性（例如，概率）的值。存储在单元格（x，y）中的值不与存储在单元格（y，x）中的值进行传递。使用矩阵中缺少的值可以使用潜在空间中的向量来计算。一旦使用矩阵被更新，可以从使用矩阵中的数据产生定向推荐。使用矩阵中的初始值可以由与实际采集相关联的数据生成。

2. 发明申请

WO2023075996A1 DEEP GRADIENT ACTIVATION MAP MODEL REFINEMENT 审中-公开
公开(公告)号：WO2023075996A1
公开(公告)日：2023-05-04
申请号：PCT/US2022/045596
申请日：2022-10-04
申请人： MICROSOFT TECHNOLOGY LICENSING, LLC.
发明人： BARKAN, Oren , ARMSTRONG, Omri , HERTZ, Amir , CACIULARU, Avi , KATZ, Ori , MALKIEL, Itzik , KOENIGSTEIN, Noam , NICE, Nir
IPC分类号： G06N3/045 , G06N3/0464 , G06N3/048 , G06V10/46 , G06V10/82
摘要： A method of generating an aggregate saliency map using a convolutional neural network. Convolutional activation maps of the convolutional neural network model are received into a saliency map generator, the convolutional activation maps being generated by the neural network model while computing the one or more prediction scores based on unlabeled input data. Each convolutional activation map corresponds to one of the multiple encoding layers. The saliency map generator generates a layer-dependent saliency map for each encoding layer of the unlabeled input data, each layer-dependent saliency map being based on a summation of element-wise products of the convolutional activation maps and their corresponding gradients. The layer-dependent saliency maps are combined into the aggregate saliency map indicating the relative contributions of individual components of the unlabeled input data to the one or more prediction scores computed by the convolutional neural network model on the unlabeled input data.

3. 发明申请

WO2022211985A1 INTERPRETING TEXT-BASED SIMILARITY 审中-公开
公开(公告)号：WO2022211985A1
公开(公告)日：2022-10-06
申请号：PCT/US2022/019218
申请日：2022-03-08
申请人： MICROSOFT TECHNOLOGY LICENSING, LLC
发明人： MALKIEL, Itzik , KOENIGSTEIN, Noam , BARKAN, Oren , GINZBURG, Dvir , NICE, Nir
IPC分类号： G06F40/284 , G06F40/30 , G06N3/00 , G06N20/00
摘要： The disclosure herein describes a system for interpreting text-based similarity between a seed item and a recommended item selected by a pre-trained language model from a plurality of candidate items based on semantic similarities between the seed item and the recommended item. The system analyzes similarity scores and contextual paragraph representations representing text-based descriptions of the seed item and recommended item to generate gradient maps and word scores representing the text-based descriptions. A model for interpreting text-based similarity utilizes the calculated gradients and word scores to match words from the seed item description with words in the recommended item description having similar semantic meaning. The word-pairs having the highest weight are identified by the system as the word-pairs having the greatest influence over the selection of the recommended item from the candidate items by the original pre-trained language model.

4. 发明申请

WO2021118736A1 TEXT-BASED SIMILARITY SYSTEM FOR COLD START RECOMMENDATIONS 审中-公开
公开(公告)号：WO2021118736A1
公开(公告)日：2021-06-17
申请号：PCT/US2020/059906
申请日：2020-11-11
申请人： MICROSOFT TECHNOLOGY LICENSING, LLC
发明人： MALKIEL, Itzik , ROIT, Pavel , KOENIGSTEIN, Noam , BARKAN, Oren , NICE, Nir
IPC分类号： G06F16/33 , G06F16/35
摘要： The disclosure herein describes a recommendation system utilizing a specialized domain-specific language model for generating cold- start recommendations in an absence of user-specific data based on a user-selection of a seed item. A generalized language model is trained using a domain-specific corpus of training data, including title and description pairs associated with candidate items in a domain-specific catalog. The language model is trained to distinguish between real title-description pairs and fake title-description pairs. The trained language model analyzes the title and description of the seed item with the title and description of each candidate item in the catalog to create a hybrid set of scores. The set of scores includes similarity scores and classification scores for the seed item title with each candidate item description and title. The scores are utilized by the model to identify candidate items maximizing similarity with the seed item for cold-start recommendation to a user.

5. 发明申请

WO2015148421A1 RECOMMENDATION SYSTEM WITH MULTI-DIMENSIONAL DISCOVERY EXPERIENCE 审中-公开
标题翻译：具有多维度发现经验的建议系统
公开(公告)号：WO2015148421A1
公开(公告)日：2015-10-01
申请号：PCT/US2015/022103
申请日：2015-03-24
申请人： MICROSOFT TECHNOLOGY LICENSING, LLC
发明人： NICE, Nir , KOENIGSTEIN, Noam , PAQUET, Ulrich , KEREN, Shahar , SITTON, Daniel , PERELSTEIN, Amit
IPC分类号： G06Q30/02 , G06Q30/06
CPC分类号： G06Q30/0631 , G06Q30/02
摘要： Example apparatus and methods perform matrix factorization (MF) on a collaborative filter based usage matrix to create a multi-dimensional latent space that embeds users, items, and features. A full distance matrix is extracted from the latent space. The full distance matrix may be extracted from the latent space by defining a distance metric between item pairs based on the multi-dimensional representation in the latent space. The full distance matrix may be populated with values computed for item pairs using the distance metric. A plurality of vectors associated with a multi-dimensional Euclidean space are produced from the full distance matrix. The plurality of vectors produce a navigable data set. The plurality of vectors may be produced in a manner that minimizes strain on the distances vectors. A representation of the navigable data set may be presented as, for example, a virtually traversable landscape that supports an interactive user experience.
摘要翻译：示例性设备和方法在基于协作过滤器的使用矩阵上执行矩阵分解（MF）以创建嵌入用户，项目和特征的多维潜在空间。从潜在空间提取全长矩阵。可以通过基于潜在空间中的多维表示定义物品对之间的距离度量，从潜在空间提取全距离矩阵。可以使用使用距离度量为项目对计算的值填充全距离矩阵。从全距离矩阵产生与多维欧几里德空间相关联的多个向量。多个向量产生可导航数据集。可以以使距离矢量上的应变最小化的方式来产生多个向量。可导航数据集的表示可以被呈现为例如支持交互式用户体验的虚拟可穿越景观。

6. 发明申请

WO2015148420A1 USER INACTIVITY AWARE RECOMMENDATION SYSTEM 审中-公开
标题翻译：用户无效性建议系统
公开(公告)号：WO2015148420A1
公开(公告)日：2015-10-01
申请号：PCT/US2015/022102
申请日：2015-03-24
申请人： MICROSOFT TECHNOLOGY LICENSING, LLC
发明人： NICE, Nir , KOENIGSTEIN, Noam , PAQUET, Ulrich , KEREN, Shahar
IPC分类号： G06Q30/02 , G06F17/30
CPC分类号： G06Q30/0631 , G06F17/16 , G06F17/30867 , G06N7/00 , G06Q30/0251 , G06Q30/0255
摘要： Example apparatus and methods perform matrix factorization (MF) on a usage matrix to create a latent space that describes similarities between users and items in the usage matrix. The usage matrix relates users to items according to a collaborative filtering approach. A cell in the usage matrix may store a value that describes whether a user has acquired an item and the strength with which the user likes an item that has been acquired. Example apparatus and methods account for negative indications analytically rather than through negative sampling. Example apparatus and methods analyze strengths in the usage matrix, analyze item popularity, analyze user popularity, compute contribution factors for items with respect to users and users with respect to items, and compute new user vectors and new item vectors that depend on the strengths, popularity, and contributions. A recommendation may consider new user vectors and new item vectors.
摘要翻译：示例性设备和方法在使用矩阵上执行矩阵分解（MF）以创建潜在空间，其描述用户和使用矩阵中的项目之间的相似性。使用矩阵根据协作过滤方法将用户与项目相关联。使用矩阵中的单元可以存储描述用户是否已经获取项目的值和用户喜欢已经获取的项目的强度。示例性的装置和方法分析性地反映阴性，而不是通过负采样。示例性设备和方法分析使用矩阵中的优势，分析项目流行度，分析用户流行度，针对项目计算用户和用户的项目的贡献因子，以及计算新的用户向量和依赖于优势的新项目向量，人气和贡献。推荐可以考虑新的用户向量和新的项目向量。

7. 发明申请

WO2016040211A1 MODIFIED MATRIX FACTORIZATION OF CONTENT-BASED MODEL FOR RECOMMENDATION SYSTEM 审中-公开
标题翻译：基于内容的建模系统模型的修改矩阵拟合
公开(公告)号：WO2016040211A1
公开(公告)日：2016-03-17
申请号：PCT/US2015/048757
申请日：2015-09-07
申请人： MICROSOFT TECHNOLOGY LICENSING, LLC
发明人： NICE, Nir , KOENIGSTEIN, Noam , KEREN, Shahar , KROSKIN, Ayelet , PAQUET, Ulrich
IPC分类号： G06Q30/02 , G06Q30/06
CPC分类号： G06Q30/0631 , G06F17/30867 , G06Q30/02
摘要： A recommendation system is implemented using modified matrix factorization on top of a content-based matrix to provide both user-to-item and item-to-item content-based recommendations while exposing the full depth of transitive relationships among recommendations. Content information such as features and characteristics may be represented in a usage matrix in which features are treated as users would be in traditional matrix factorization. Matrix factorization is applied to the "features-as-users" matrix to build a content-based model in which features and items are embedded in a low dimension latent space. User history is employed for system training by locating user vectors within the latent space. Recommendations that are near to the vector can be provided to the users along with explanations (e.g., a recommendation is given because of an item's proximity to a particular feature).
摘要翻译：在基于内容的矩阵之上使用修改矩阵分解来实现推荐系统，以提供用户对项目和项目到项目基于内容的建议，同时暴露推荐之间的传递关系的全部深度。诸如特征和特征的内容信息可以在使用矩阵中表示，其中特征被视为用户将处于传统矩阵分解中。矩阵分解被应用于“特征为用户”矩阵，以构建基于内容的模型，其中特征和项目嵌入在低维潜力空间中。通过将用户向量定位在潜在空间内，用户历史被用于系统训练。靠近向量的建议可以与说明一起提供给用户（例如，由于项目与特定特征的接近而给出推荐）。

8. 发明申请

WO2015148422A1 RECOMMENDATION SYSTEM WITH DUAL COLLABORATIVE FILTER USAGE MATRIX 审中-公开
标题翻译：具有双协同滤波器使用矩阵的建议系统
公开(公告)号：WO2015148422A1
公开(公告)日：2015-10-01
申请号：PCT/US2015/022104
申请日：2015-03-24
申请人： MICROSOFT TECHNOLOGY LICENSING, LLC
发明人： NICE, Nir , KOENIGSTEIN, Noam , PAQUET, Ulrich , KEREN, Shahar
IPC分类号： G06Q30/02 , G06Q30/06
CPC分类号： G06F17/30699 , G06Q30/02 , G06Q30/0631
摘要： Example apparatus and methods perform matrix factorization (MF) on a usage matrix to create a latent space that describes similarities between users and items and between items and items in the usage matrix. The usage matrix relates users to items according to a collaborative filtering approach. A cell in the usage matrix may store a value that describes whether a user has acquired an item and the strength with which the user likes an item that has been acquired. The latent item space may reflect true relationships between items represented in the usage matrix and those relationships may be proportional to the strength in the usage matrix. The strength of the relationship may be encoded using continuous data that measures, for example, the amount of time a video game has been played, the amount of time content has been viewed, or other continuous or cumulative engagement measurements.
摘要翻译：示例性装置和方法在使用矩阵上执行矩阵分解（MF）以创建描述用户和项目之间以及使用矩阵中的项目和项目之间的相似性的潜在空间。使用矩阵根据协作过滤方法将用户与项目相关联。使用矩阵中的单元可以存储描述用户是否已经获取项目的值和用户喜欢已经获取的项目的强度。潜在项目空间可以反映在使用矩阵中表示的项目之间的真实关系，并且这些关系可以与使用矩阵中的强度成比例。关系的强度可以使用测量例如视频游戏已经播放的时间量，已经观看的时间量或其他连续或累积的接合测量的连续数据进行编码。

9. 发明申请

WO2023076008A1 DEEP SELF-ATTENTION MATRIX MODEL REFINEMENT 审中-公开
公开(公告)号：WO2023076008A1
公开(公告)日：2023-05-04
申请号：PCT/US2022/045829
申请日：2022-10-06
申请人： MICROSOFT TECHNOLOGY LICENSING, LLC.
发明人： BARKAN, Oren , HAUON, Edan , KATZ, Ori , CACIULARU, Avi , MALKIEL, Itzik , ARMSTRONG, Omri , HERTZ, Amir , KOENIGSTEIN, Noam , NICE, Nir
IPC分类号： G06N3/0455 , G06N3/044
摘要： A computing system scores importance of a number of tokens in an input token sequence to one or more prediction scores computed by a neural network model on the input token sequence. The neural network model includes multiple encoding layers. Self-attention matrices of the neural network model are received into an importance evaluator. The self-attention matrices are generated by the neural network model while computing the one or more prediction scores based on the input token sequence. Each self-attention matrix corresponds to one of the multiple encoding layers. The importance evaluator generates an importance score for one or more of the tokens in the input token sequence. Each importance score is based on a summation as a function of the self-attention matrices, the summation being computed across the tokens in the input token sequence, across the self-attention matrices, and across the multiple encoding layers in the neural network model.

10. 发明申请

WO2023075944A1 ANCHOR-BASED COLLABORATIVE FILTERING 审中-公开
公开(公告)号：WO2023075944A1
公开(公告)日：2023-05-04
申请号：PCT/US2022/043398
申请日：2022-09-14
申请人： MICROSOFT TECHNOLOGY LICENSING, LLC.
发明人： BARKAN, Oren , HIRSCH, Roy , KATZ, Ori , CACIULARU, Avi , KOENIGSTEIN, Noam , NICE, Nir
IPC分类号： G06Q30/02 , G06Q30/06
摘要： An anchor-based collaborative filtering system receives a training dataset including user-item interactions each identifying a user and an item that the user has positively interacted with. The system defines a vector space and distributes the items of the training dataset within the vector based on a determined similarity of the items. The system further defines a set of taste anchors that are each associated in memory with a subgroup of the items in a same neighborhood of the vector space. To make a recommendation to an individual user, the system identifies an anchor-based representation for the individual user that includes a subset of the defined taste anchors that best represents the types of items that the user has favorably interacted with in the past. The taste anchors included in the identified anchor-based representation for the individual user are used to make recommendations to the user in the future.

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