Microsoft Technology Licensing, LLC

Haifeng Zhao

Liang Zhang

Yang Chen

John P Moore

Chencheng Wu

Jiashuo Wang

Ankit Gupta

Souvik Ghosh

Jing Wang

Xiaohai Zhang

Xiaojing Chen

Meng Meng

Haowen Cao

Aparna Krishnan

Anand Kishore

Henry Heyburn Pistell

Keyi Yu

Mira Thambireddy

Michael A Menarguez

Techniques for a flexible data security and machine learning system for merging third-party data are provided. In one technique, the system receives a data set from a third-party entity and receives selection data that indicates that the third-party entity selected a set of data security policies that includes an encryption option and a data mixing option from among multiple data mixing options. In response to receiving the selection data, the system stores data that associates the set of data security policies with the data set, encrypts the data set according to the encryption option, and persistently stores the encrypted data set. Later, the system decrypts the encrypted data set in volatile memory, generates, based on the data mixing option, training data based on the decrypted version of the data set, trains a machine-learned model based on the training data, and stores the machine-learned model in association with the data set.

Flexible data security and machine learning system for merging third-party data

Computer-implemented techniques for enhanced tagging of natural language queries that are initially segmented and tagged by a named entity recognition system. By doing so, enhanced tagging of a natural language query that represents a deeper understanding of the query is provided. The enhanced tagging improves the operation of search engines that use the enhanced tags by enabling the search engine to identify and return more relevant search results in answers to natural language queries.

Enhanced natural language query segment tagging

The disclosed embodiments provide a system for performing sequence modeling for searches. During operation, the system obtains a sequence of jobs associated with activity by a member of an online system. Next, the system applies a word embedding model of a set of job histories to attributes of individual jobs in the sequence of jobs to produce embeddings for the individual jobs. The system then generates a set of power means from the embeddings. Finally, the system outputs the set of power means as an encoded representation of the sequence of jobs, wherein the set of power means is used in generating job recommendations related to the member.

Sequence modeling for searches

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.

Entity resolution techniques for matching entity records from different data sources

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Haifeng Zhao