Sequence modeling for searches
AssigneeMicrosoft Technology Licensing, LLC
InventorsMeng Meng, Haifeng Zhao
Life of this patent
Estimating the term…
Abstract
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.
Claims16
A computer-implemented method, comprising:
- using a word embedding model, generating word embeddings for words used as job attributes, the word embedding model trained to generate the word embeddings with training data comprising job attributes obtained from member profiles of members of an online system, the job attributes comprising one or more of job titles, industries, company names, schools, and/or fields of study;
- processing a first member profile of a first member of the online system to identify one or more words associated with each job in a sequence of jobs associated with the first member;
- converting the one or more words associated with each job in the sequence of jobs to a pre-trained word embedding generated by the word embedding model;
- generating a set of power means from the pre-trained word embeddings;
- and
- outputting the set of power means as a fixed length encoded representation of the sequence of jobs.
A system, comprising:
- one or more processors;
- and
- memory storing instructions that, when executed by the one or more processors, cause the system to:
- using a word embedding model, generate word embeddings for words used as job attributes, the word embedding model trained to generate the word embeddings with training data comprising job attributes obtained from member profiles of members of an online system, the job attributes comprising one or more of job titles, industries, company names, schools, and/or fields of study;
- process a first member profile of a first member of the online system to identify one or more words associated with each job in a sequence of jobs associated with the first member;
- convert the one or more words associated with each job in the sequence of jobs to a pre-trained word embedding generated by the word embedding model;
- generate a set of power means from the pre-trained word embeddings;
- and
- output the set of power means as a fixed length encoded representation of the sequence of jobs.
A non-transitory computer-readable storage medium storing instructions that when executed by a computer cause the computer to perform a method, the method comprising:
- using a word embedding model, generating word embeddings for words used as job attributes, the word embedding model trained to generate the word embeddings with training data comprising job attributes obtained from member profiles of members of an online system, the job attributes comprising one or more of job titles, industries, company names, schools, and/or fields of study;
- processing a first member profile of a first member of the online system to identify one or more words associated with each job in a sequence of jobs associated with the first member;
- converting the one or more words associated with each job in the sequence of jobs to a pre-trained word embedding generated by the word embedding model;
- generating a set of power means from the pre-trained word embeddings;
- and
- outputting the set of power means as a fixed length encoded representation of the sequence of jobs.
Dependencies are inferred from the claim text.