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1. 发明申请

WO2014092771A1 SYSTEM AND METHOD FOR SERVING ELECTRONIC CONTENT 审中-公开
标题翻译：用于服务电子内容的系统和方法
公开(公告)号：WO2014092771A1
公开(公告)日：2014-06-19
申请号：PCT/US2013/044553
申请日：2013-06-06
申请人： LINKEDIN CORPORATION
发明人： DUBEY, Sanjay, S. , AGARWAL, Deepak , WEI, Kai , ZHANG, Liang , PEI, Lihong
IPC分类号： G06F17/30
CPC分类号： G06Q30/0283 , G06Q30/02 , G06Q30/0251
摘要： A system and methods are provided for serving content in response to content queries or requests. When a request is received, for content to be presented to a specified user, candidate content items are identified, possibly based on matches between attributes of the user and attributes of the items' target audiences. For each item, a history indicating the frequency and/or recency with which impressions of the candidate item were previously presented to the user is retrieved and used to determine a modifier value, which is applied to a calculated or generated probable click-through-rate (pCTR) to produce a modified probability that the user would act on the item if it is served to him or her. Each item's estimated value is computed by multiplying a bid associated with the item and the modified probability; the results are ranked and the top-ranked item(s) are served.
摘要翻译：提供了用于响应于内容查询或请求来服务内容的系统和方法。当接收到请求时，对于要呈现给指定用户的内容，可以基于用户的属性和项目的目标受众的属性之间的匹配来识别候选内容项。对于每个项目，检索指示先前向用户呈现候选项目的展示的频率和/或新近度的历史，并用于确定修改值，其被应用于计算或生成的可能的点击率（pCTR），以产生修改的概率，如果用户对其提供服务，则用户将对其进行操作。每个项目的估计值通过将与项目相关联的出价和修改的概率相乘来计算; 排名结果排名第一，并排名第一。

2. 发明申请

WO2018111380A1 DETERMINING INDUSTRY SIMILARITIES TO ENHANCE JOB SEARCHING 审中-公开
公开(公告)号：WO2018111380A1
公开(公告)日：2018-06-21
申请号：PCT/US2017/054092
申请日：2017-09-28
申请人： LINKEDIN CORPORATION
发明人： GROVER, Aman , ARYA, Dhruv , VENKATARAMAN, Ganesh , MCMANUS, Kimberly , ZHANG, Liang
IPC分类号： G06F17/30 , G06Q10/10 , G06Q50/00
摘要： Methods, systems, and computer programs are presented for expanding a job search that includes an industry by adding other similar industries. A method identifies job titles of members in a social network and performs, utilizing a machine-learning program, semantic analysis of the job titles to identify similarity coefficients among the job titles. The machine-learning program utilizes social network data to identify the similarity coefficients. Further, the method includes an operation for receiving a job search query, from a first member, including a query job title, and for expanding the job search query with job titles that are similar to the query job title. The method further includes operations for executing the expanded job search query to generate a plurality of job results, and for causing presentation on a display of one or more of the top job results.

3. 发明申请

WO2018023329A1 QUALITY INDUSTRY CONTENT MIXED WITH FRIEND'S POSTS IN SOCIAL NETWORK 审中-公开
标题翻译：质量工业内容与朋友在社交网络中的职位相融合
公开(公告)号：WO2018023329A1
公开(公告)日：2018-02-08
申请号：PCT/CN2016/092667
申请日：2016-08-01
申请人： LINKEDIN CORPORATION
发明人： ZHU, Lin , WANG, Di , ZHAO, Sheng , LIU, Yang , CHEN, Shu , ZHANG, Liang
IPC分类号： G06F17/30
CPC分类号： G06F16/24578 , G06F16/00 , G06F16/9535 , G06K9/6223 , G06N20/00 , G06Q50/01
摘要： Methods, systems, and programs are provided for presenting professional content in a user feed. The user feed is populated with industry-wise content, using relevance-driven technologies to select the best relevant industry content, while solving the problem of low-content availability for users with few connections. Professional posts created by users in a social network are identified, where each user is associated with an industry as configured in a profile of the user. A score for each identified professional post is calculated and the professional posts are sorted based on their scores. After detecting a request for data to present in a first user feed to a first user that is associated with a first industry, the server selects professional posts based on the industry of the user that created each post. The selected professional posts are then merged with other types of content and presented in the first user feed.
摘要翻译：
提供方法，系统和程序，用于在用户源中呈现专业内容。用户Feed中充满了行业内容，使用相关性驱动的技术来选择最佳相关行业内容，同时解决了连接较少的用户内容可用性低的问题。识别用户在社交网络中创建的专业帖子，其中每个用户与在用户的配置文件中配置的行业相关联。计算每个确定的专业职位的分数，并根据他们的分数对专业职位进行排序。在检测到针对与第一行业相关联的第一用户的第一用户馈送中呈现的数据的请求之后，服务器基于创建每个帖子的用户的行业来选择专业帖子。选定的专业帖子会与其他类型的内容合并并显示在第一个用户Feed中。

4. 发明申请

WO2018010147A1 USER FEED WITH PROFESSIONAL AND NONPROFESSIONAL CONTENT 审中-公开
标题翻译：用户提供专业和非专业内容
公开(公告)号：WO2018010147A1
公开(公告)日：2018-01-18
申请号：PCT/CN2016/090063
申请日：2016-07-14
申请人： LINKEDIN CORPORATION
发明人： ZHANG, Liang , ZHU, Lin , WANG, Di , ZHAO, Sheng , LIU, Yang , CHEN, Shu
IPC分类号： G06F17/30
CPC分类号： G06K9/6223 , G06F16/3344 , G06F17/2785 , G06N20/00 , G06Q30/02 , G06Q30/0269 , G06Q50/01
摘要： Methods, systems, and computer programs are presented for optimizing the content of a user feed that includes professional and nonprofessional posts. One method includes an operation for training a machine-learning classifier to classify posts of a social website as professional or nonprofessional posts based on a plurality of features that include a cluster assigned to each post. Posts are identified for placement in a user feed of the social website, each post being associated with a score, and each post is assigned to one of the clusters based on the semantic meaning of the words in the post. The method further includes operations for invoking the machine-learning classifier to classify each post as a professional or a nonprofessional post, and for increasing the scores of the posts classified as professional posts. The posts are ranked for presentation in the user feed based on the score of each post.
摘要翻译：介绍方法，系统和计算机程序，用于优化包含专业和非专业帖子的用户供稿内容。一种方法包括用于训练机器学习分类器以基于包括分配给每个帖子的群集的多个特征来将社交网站的帖子分类为专业或非专业帖子的操作。确定帖子置于社交网站的用户提要中，每个帖子与分数相关联，并且基于帖子中单词的语义含义将每个帖子分配给其中一个群组。该方法还包括用于调用机器学习分类器以将每个帖子分类为专业或非专业帖子并用于增加被分类为专业帖子的帖子的分数的操作。根据每篇文章的得分，这些帖子将在用户Feed中进行排名。

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