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1. 发明授权

US09361584B2 Recommender evaluation based on tokenized messages 有权
公开(公告)号：US09361584B2
公开(公告)日：2016-06-07
申请号：US14855476
申请日：2015-09-16
申请人： LinkedIn Corporation
发明人： Christian Posse , Anmol Bhasin , Wing Li
IPC分类号： G06N5/02 , G06N5/04 , G06Q30/02 , G06F17/27
CPC分类号： G06N5/04 , G06F17/2785 , G06N5/02 , G06Q30/02
摘要： A machine may implement a recommender that provides recommendations to users. The machine may be configured to present a first version of the recommender configured by various parameters. A user may submit a message to the machine, and the machine may identify a parameter among the various parameters by tokenizing the message and identifying the parameter among the tokens. The machine may then generate a second version of the recommender by modifying the parameter and configuring the second version according to the modified parameter. The machine may then present the first and second versions of the recommender contemporaneously two different portions of the users. By tokenizing a further message received from the users, the machine may evaluate the first and second versions and determine whether the second version is a replacement of the first version.

2. 发明申请

US20150163190A1 SUGGESTED OUT OF NETWORK COMMUNICATION RECIPIENTS 有权
标题翻译：建议网络通信接收方
公开(公告)号：US20150163190A1
公开(公告)日：2015-06-11
申请号：US14621633
申请日：2015-02-13
申请人： LinkedIn Corporation
发明人： Junghoon (Andrew) Ahn , Abhishek Gupta , Christian Posse , Anmol Bhasin , Yurong Shi , Jian Li , Jacob Kessler
IPC分类号： H04L12/58 , H04L29/08
CPC分类号： G06F17/30867 , G06F17/30861 , G06F17/30991 , H04L51/32 , H04L67/22 , H04L67/306
摘要： Disclosed in some examples are methods, systems and machine readable medium for recommending an out-of-network communication by determining a set of potential recommended members of a social networking service based upon one or more recommendation criteria. In some examples the recommendation criteria may include: a profile similarity to a previous target of an out-of-network communication, a degree of correspondence between an interest and intent of the sending member, and a likelihood of response.
摘要翻译：在一些示例中公开了用于通过基于一个或多个推荐标准确定社交网络服务的一组潜在推荐成员来推荐网络外通信的方法，系统和机器可读介质。在一些示例中，推荐标准可以包括：与网络外通信的先前目标的简档相似性，发送成员的兴趣和意图之间的对应程度以及响应的可能性。

3. 发明申请

US20150134745A1 METHODS AND SYSTEMS FOR IDENTIFYING MEMBER PROFILES SIMILAR TO A SOURCE MEMBER PROFILE 有权
标题翻译：用于识别类似于会员资料的会员资料的方法和系统
公开(公告)号：US20150134745A1
公开(公告)日：2015-05-14
申请号：US14602661
申请日：2015-01-22
申请人： LinkedIn Corporation
发明人： Christian Posse , Abhishek Gupta , Anmol Bhasin , Monica Rogati
IPC分类号： H04L29/08
CPC分类号： G06F17/3053 , G06F17/30554 , G06F17/30867 , H04L67/22 , H04L67/306
摘要： Techniques for identifying and presenting member profiles similar to a source member profile are described. With some embodiments, a general recommendation engine is used to extract features from member profiles, and then store the extracted features, including any computed, derived or retrieved profile features, in an enhanced member profile. In real-time, the general recommendation engine processes client requests to identify member profiles similar to a source member profile by comparing select profile features stored in the enhanced member profile with corresponding profile features of the source member profile, where the comparison results in several similarity sub-scores that are then combined in accordance with directives set forth in a configuration file. Finally, the member profiles with the highest similarity scores corresponding with the user-selected member profile are selected, and in some instances, presented to a user.
摘要翻译：描述用于识别和呈现类似于源成员简档的成员简档的技术。利用一些实施例，使用通用推荐引擎从成员简档中提取特征，然后将所提取的特征（包括任何计算的，导出的或检索到的简档特征）存储在增强的成员简档中。实时地，一般推荐引擎通过将存储在增强成员简档中的选择简档特征与源成员简档的相应简档特征进行比较来处理客户端请求以识别类似于源成员简档的成员简档，其中比较导致几种相似性然后根据配置文件中列出的指令组合子分数。最后，选择与用户选择的成员简档相对应的具有最高相似性得分的成员简档，并且在某些情况下呈现给用户。

4. 发明申请

US20150081576A1 GENERATING A SUPPLEMENTAL DESCRIPTION OF AN ENTITY 有权
标题翻译：生成实体的补充说明
公开(公告)号：US20150081576A1
公开(公告)日：2015-03-19
申请号：US14496914
申请日：2014-09-25
申请人： LinkedIn Corporation
发明人： Anmol Bhasin , Jiong Wang , Abhishek Gupta , Alexis Pribula , Ramesh Dommeti , Christian Posse
IPC分类号： G06F17/30 , H04L29/08 , G06Q50/00
CPC分类号： G06Q50/01 , G06F17/3053 , G06F17/30554 , G06F17/30702 , G06F17/30867 , G06Q10/10 , G06Q10/1053 , H04L67/30 , H04L67/306
摘要： A statistically overrepresented token in the descriptions of users associated with a target entity may be descriptive of the target entity. This may be true regardless of whether a primary description of the entity includes the overrepresented token. Accordingly, the entity description machine may access multiple descriptions of multiple users associated with the target entity. A portion of the multiple descriptions may each include a token descriptive of the target entity and of a subset of the multiple users. The entity description machine may determine that the token is overrepresented among the tokens within the multiple descriptions and generate a supplemental description of the target entity, where the supplemental description includes the overrepresented token. Once the supplemental description is generated, the entity description machine may use the supplemental description in referencing the target entity.
摘要翻译：在与目标实体相关联的用户的描述中的统计上超表示的标记可以描述目标实体。无论实体的主要描述包括超额代表的令牌，这可能都是真的。因此，实体描述机器可以访问与目标实体相关联的多个用户的多个描述。多个描述的一部分可以各自包括描述目标实体和多个用户的子集的令牌。实体描述机器可以确定令牌在多个描述内的令牌中被超额表示，并且生成目标实体的补充描述，其中补充描述包括过度表示的令牌。一旦生成补充描述，实体描述机器可以在引用目标实体时使用补充描述。

5. 发明授权

US10586167B2 Regularized model adaptation for in-session recommendations 有权
公开(公告)号：US10586167B2
公开(公告)日：2020-03-10
申请号：US14864499
申请日：2015-09-24
申请人： LinkedIn Corporation
发明人： Xu Miao , Yitong Zhou , Joel D. Young , Lijun Tang , Anmol Bhasin
IPC分类号： G06N20/00 , G06Q10/00 , G06Q30/02 , G06Q10/06 , G06N20/20
摘要： The disclosed embodiments provide a method and system for performing regularized model adaptation for in-session recommendations. During operation, the system obtains, from a server, a first global version of a statistical model. During a first user session with a user, the system improves a performance of the statistical model by using the first global version to output one or more recommendations to the user and using the first global version and user feedback from the user to create a first personalized version of the statistical model. At an end of the first user session, the system transmits an update containing a difference between the first personalized version and the first global version to the server for use in producing a second global version of the statistical model by the server.

6. 发明授权

US10380500B2 Version control for asynchronous distributed machine learning 有权
公开(公告)号：US10380500B2
公开(公告)日：2019-08-13
申请号：US14864474
申请日：2015-09-24
申请人： LinkedIn Corporation
发明人： Xu Miao , Yitong Zhou , Joel D. Young , Lijun Tang , Anmol Bhasin
IPC分类号： G06N20/00 , G06F8/71 , G06F8/65
摘要： A system and method for managing asynchronously receiving updates and merging updates into global versions of a statistical model using version control are disclosed. During operation, the system transmits a first global version of a statistical model to a set of client computer systems. Next, the system obtains, from a first subset of the client computer systems, a first set of updates to the first global version. The system then merges the first set of updates into a second global version of the statistical model. Finally, the system transmits the second global version to the client computer systems asynchronously from receiving a second set of updates to the first and/or second global versions from a second subset of the client computer systems.

7. 发明授权

US09372930B2 Generating a supplemental description of an entity 有权
标题翻译：生成实体的补充说明
公开(公告)号：US09372930B2
公开(公告)日：2016-06-21
申请号：US14496914
申请日：2014-09-25
申请人： LinkedIn Corporation
发明人： Anmol Bhasin , Jiong Wang , Abhishek Gupta , Alexis Pribula , Ramesh Dommeti , Christian Posse
IPC分类号： G06F17/30 , G06Q10/10 , G06Q50/00 , H04L29/08
CPC分类号： G06Q50/01 , G06F17/3053 , G06F17/30554 , G06F17/30702 , G06F17/30867 , G06Q10/10 , G06Q10/1053 , H04L67/30 , H04L67/306
摘要： A statistically overrepresented token in the descriptions of users associated with a target entity may be descriptive of the target entity. This may be true regardless of whether a primary description of the entity includes the overrepresented token. Accordingly, the entity description machine may access multiple descriptions of multiple users associated with the target entity. A portion of the multiple descriptions may each include a token descriptive of the target entity and of a subset of the multiple users. The entity description machine may determine that the token is overrepresented among the tokens within the multiple descriptions and generate a supplemental description of the target entity, where the supplemental description includes the overrepresented token. Once the supplemental description is generated, the entity description machine may use the supplemental description in referencing the target entity.
摘要翻译：在与目标实体相关联的用户的描述中的统计上超表示的标记可以描述目标实体。无论实体的主要描述包括超额代表的令牌，这可能都是真的。因此，实体描述机器可以访问与目标实体相关联的多个用户的多个描述。多个描述的一部分可以各自包括描述目标实体和多个用户的子集的令牌。实体描述机器可以确定令牌在多个描述内的令牌中被超额表示，并且生成目标实体的补充描述，其中补充描述包括过度表示的令牌。一旦生成补充描述，实体描述机器可以在引用目标实体时使用补充描述。

8. 发明授权

US09082084B2 Facilitating machine learning in an online social network 有权
标题翻译：促进在线社交网络中的机器学习
公开(公告)号：US09082084B2
公开(公告)日：2015-07-14
申请号：US13931109
申请日：2013-06-28
申请人： LinkedIn Corporation
发明人： Paul T. Ogilvie , Xiangrui Meng , Anmol Bhasin , Trevor A. Walker
IPC分类号： G06N99/00 , G06Q50/00
CPC分类号： G06N99/005 , G06Q50/01
摘要： Automatic machine-learning processes and systems for an online social network are described. During operation of the online social network, a system can automatically collect labeled training events, obtain snapshots of raw entity data associated with subjects from the collected training events, produce training examples by generating features for each training event using the snapshots of entity data and current entity data, and split the training examples into a training set and a test set. Next, the system can use a machine-learning technique to train a set of models and to select the best model based on one or more evaluation metrics using the training set. The system can then evaluate the performance of the best model on the test set. If the performance of the best model satisfies a performance criterion, the system can use the best model to predict responses for the online social network.
摘要翻译：描述了用于在线社交网络的自动机器学习过程和系统。在线社交网络运行期间，系统可以自动收集标签的训练事件，从收集的训练事件中获取与主体相关的原始实体数据的快照，通过使用实体数据和当前的快照为每个训练事件生成特征来产生训练示例实体数据，并将训练样本分为训练集和测试集。接下来，系统可以使用机器学习技术来训练一组模型，并且基于使用训练集合的一个或多个评估度量来选择最佳模型。然后，系统可以评估测试集上最佳模型的性能。如果最佳模型的性能满足性能标准，则系统可以使用最佳模型来预测在线社交网络的响应。

9. 发明申请

US20150134575A1 USING DATA OBTAINED FROM AN ONLINE SOCIAL NETWORK TO OPTIMIZE SUBSCRIPTION OFFERS 有权
标题翻译：使用在线社交网络获取的数据来优化订阅优惠
公开(公告)号：US20150134575A1
公开(公告)日：2015-05-14
申请号：US14079300
申请日：2013-11-13
申请人： LinkedIn Corporation
发明人： Jonathan D. Traupman , Tarun Kumar , Venu Javarappa , Anmol Bhasin , Lizabeth Li , Yurong Shi
IPC分类号： G06N99/00 , G06N5/04
CPC分类号： G06N99/005 , G06N5/04
摘要： The disclosed embodiments relate to a system that uses data from an online social network to optimize subscription offers. During operation of the online social network, the system gathers data associated with subscription offers that were presented to members of the online social network, including information about which subscription offers were converted. Next, the system uses a machine-learning technique to train a model based on the gathered data. Finally, the system uses the trained model to select subscription offers to present to a member of the online social network.
摘要翻译：所公开的实施例涉及使用来自在线社交网络的数据来优化订阅提供的系统。在线社交网络运行期间，系统收集与在线社交网络成员提交的订阅优惠相关的数据，包括哪些订阅优惠转换的信息。接下来，系统使用机器学习技术来基于收集的数据来训练模型。最后，系统使用经过训练的模型来选择向在线社交网络成员呈现的订阅优惠。

10. 发明申请

US20170364521A1 SUGGESTING CANDIDATE PROFILES SIMILAR TO A REFERENCE PROFILE 审中-公开
公开(公告)号：US20170364521A1
公开(公告)日：2017-12-21
申请号：US15692084
申请日：2017-08-31
申请人： LinkedIn Corporation
发明人： Christian Posse , Abhishek Gupta , Anmol Bhasin , Monica Rogati
IPC分类号： G06F17/30 , H04L29/08
CPC分类号： G06F16/24578 , G06F16/248 , G06F16/9535 , H04L67/22 , H04L67/306
摘要： Techniques for identifying and presenting member profiles similar to a source member profile are described. With some embodiments, a general recommendation engine is used to extract features from member profiles, and then store the extracted features, including any computed, derived or retrieved profile features, in an enhanced member profile. In real-time, the general recommendation engine processes client requests to identify member profiles similar to a source member profile by comparing select profile features stored in the enhanced member profile with corresponding profile features of the source member profile, where the comparison results in several similarity sub-scores that are then combined in accordance with directives set forth in a configuration file. Finally, the member profiles with the highest similarity scores corresponding with the user-selected member profile are selected, and in some instances, presented to a user.

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