公开(公告)号：US11188950B2

公开(公告)日：2021-11-30

申请号：US15253702

申请日：2016-08-31

Applicant: Microsoft Technology Licensing, LLC

Inventor： Haishan Liu ,  David Merrill Pardoe ,  Kun Liu ,  Manoj Rameshchandra Thakur ,  Kancheng Cao ,  Chongzhe Li

IPC: G06Q30/02 ,  G06Q50/00

Abstract: The present disclosure describes various embodiments of methods, systems, and machine-readable mediums which may be used in conjunction with a campaign for distributing content to users of the social network. Among other things, embodiments of the present disclosure provide a number of advantages over conventional systems for content distribution, including a simplified targeting process and increased reach (i.e. distribution) for content providers among users of a social network, as well as improving the utilization of an inventory of content and higher and more efficient engagement with such content by users of the social network.

公开(公告)号：US11188937B2

公开(公告)日：2021-11-30

申请号：US15994481

申请日：2018-05-31

Applicant: Microsoft Technology Licensing, LLC

Inventor： Huiji Gao ,  Jianling Zhong ,  Haishan Liu

IPC: G06Q30/00 ,  G06Q30/02 ,  H04L29/08

Abstract: Techniques for extracting features of entities and targets that can be applied in a set of applications, such as entity selection prediction, audience expansion, feed relevance, and job recommendation. In one technique, entity interaction data is stored that indicates, for each of multiple entities, one or more targets that are associated with items with which the entity interacted. Token association data is stored that indicates, for each of multiple tokens, one or more targets that are associated with the token. Then, using one or more machine learning techniques, entity embeddings and target embeddings are generated based on the entity interaction data and the token association data. Later, a request for content is received from a particular entity. Based on at least one entity embedding, a content item for the particular entity is identified. The content item is transferred over a computer network and presented to the particular entity.

公开(公告)号：US20190034783A1

公开(公告)日：2019-01-31

申请号：US15664214

申请日：2017-07-31

Applicant: Microsoft Technology Licensing, LLC

Inventor： Haishan Liu ,  Huiji Gao ,  Jianling Zhong

IPC: G06N3/04 ,  G06N3/08

Abstract: Herein are techniques to use an artificial neural network to score the relevance of content items for a target and techniques to rank the content items based on their scores. In embodiments, a computer uses a plurality of expansion techniques to identify expanded targets for a content item. For each of the expanded targets, the computer provides inputs to an artificial neural network to generate a relevance score that indicates a relative suitability of the content item for that target. The computer ranks the expanded targets based on the relevance score generated for each of the expanded targets. Based on the ranking, the computer selects a subset of targets from the available expanded targets as the expanded targets for whom the content item is potentially most relevant. The computer stores an association between the content item and each target in the subset of expanded targets.