Microsoft Technology Licensing, LLC

Meng Meng

Vitaly Abdrashitov

Cagri Ozcaglar

Ada Cheuk Ying Yu

Daniel Sairom Krishnan Hewlett

Swanand Wakankar

Haifeng Zhao

Gheorghe Muresan

Sriram Vasudevan

Techniques for incorporating sequence encoders into machine-learned models where the sequence encoders operate on bag of words (BOW) input are provided. Tokens that are associated with online activities of an entity are identified. Machine-learned embeddings that correspond to the tokens are identified. Based on one or more ordering criteria that are independent of the temporal occurrence of the online activities of the entity, an order of the machine-learned embeddings is determined. Based on the order, the machine-learned embeddings are inputted to a sequence encoder that generates output. Based on the output, a machine learned model that includes the sequence encoder generates a score. A content item is selected based on the score. The content item is transmitted over a computer network to a computing device.

Machine-learned models incorporating sequence encoders that operate on bag of words input

The disclosed embodiments provide a system for processing data. During operation, the system obtains labels for entities found in portions of text in a first set of jobs. Next, the system inputs the portions of text and the labels as training data for a machine learning model. The system then applies the machine learning model to a second set of jobs to generate predictions of additional entities in additional portions of text in the second set of jobs. Finally, the system creates, based on the predictions, an index containing mappings of the additional entities to subsets of the second set of jobs in which the additional entities are found.

Semantic matching of search terms to results

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

Methods, systems, and computer programs are presented for improved search methods based on career transition embeddings. One method includes an operation for generating career transition vectors for members of an online service, each career transition vector comprising identifiers associated with the career transitions of each member. The method further includes operations for performing a similarity analysis of the career transition vectors to generate an embedding vector for each identifier, detecting access of a first member to a job search user interface, and selecting one or more top embedding vectors based on one or more embedding vectors of the first member. One or more search starters associated with the one or more top embedding vectors are generated, and the one or more search starters are presented on the job search user interface.

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Meng Meng