公开(公告)号：US11080272B2

公开(公告)日：2021-08-03

申请号：US16457666

申请日：2019-06-28

Applicant: Microsoft Technology Licensing, LLC

Inventor： Yang Chen ,  Liang Zhang ,  Haifeng Zhao ,  Jiashuo Wang ,  Aparna Krishnan ,  Anand Kishore ,  Chencheng Wu ,  John P. Moore

IPC: G06F16/242 ,  G06F16/25 ,  G06F16/2457

Abstract: Entity resolution techniques for matching entity records from different data sources are provided. In one technique, an entity record from a source database is identified along with multiple data items included therein. Each data item corresponds to an attribute of multiple source attributes. For one of the data items that corresponds to a first source attribute, multiple target attributes are identified. A first query is generated that includes the data items and associates the data item with each of the multiple target attributes. A second query that is different than the first query is also generated. Two searches are performed of a target database: one based on the first query and the other based on the second query. A scoring model generates multiple scores, one for each search result. It is determined whether the entity record matches an entity record in the target database based on the set of scores.

公开(公告)号：US11768843B1

公开(公告)日：2023-09-26

申请号：US17752753

申请日：2022-05-24

Applicant: Microsoft Technology Licensing, LLC

Inventor： Peng Du ,  Mathew H. Teoh ,  Rupesh Gupta ,  Anand Kishore

IPC: G06F16/2457 ,  G06F16/248

CPC classification number: G06F16/24578 ,  G06F16/248 ,  G06F16/24573

Abstract: Embodiments include technologies to apply at least one machine learning model to features of a search query, features of a searcher user, features of a searchee content item, and features of a searchee user, produce a first outcome prediction that represents a probability of a first objective relating to engagement of the searcher user with a content item in an online system and a second outcome prediction that represents a probability of a second objective relating to engagement of the searchee user with the online system responsive to the engagement of the searcher user with the content item, apply a multi-objective optimization solver to the first objective, the second objective and an outcome prediction that is a combination of the first outcome prediction and the second outcome prediction, and generate a serving function for a search engine based on the first objective, the second objective, and the outcome prediction.