Microsoft Technology Licensing, LLC

Walmart Apollo, LLC

Avigilon Fortress Corporation

Feng Guo

Qi He

Jaewon Yang

Fei Chen

Baoxu Shi

Shankara Bhargava Subramanya

Madhusudan Mathihalli

Girish Natarajan

Guoxun Yuan

Peide Zhong

Jagannath Putrevu

Dan Shacham

Tae Eun Choe

Mun Wai Lee

Ji Yan

Mustafa Emre Kazdagli

Shan Li

Hongli Deng

Feishe Chen

Weizhi Meng

Wei Kang

Angie Zhu

Shuai Wang

Described herein is a technique for mapping the raw text of a job title of an online job posting to an entity embedding, associated with an entity or entry of a title taxonomy. The raw text of the job title is first encoded to generate a multilingual word embedding in a multilingual word embedding space. Then, the vector representation of the job title, as represented in the multilingual word embedding space is translated, using a neural network, to a vector representation of the job title in the entity embedding space. Finally, a nearest neighbor search is performed to identify an entity embedding associated with an entity or entry in the title taxonomy that has a vector representation that is closest in distance to the vector output by the neural network.

Techniques for improving standardized data accuracy

In an example embodiment, a screening question-based online screening mechanism is provided to assess job applicants automatically. More specifically, job-specific questions are automatically generated and asked to applicants to assess the applicants using the answers they provide. Answers to these questions are more recent than facts contained in a user profile and thus are more reliable measures of an appropriateness of an applicant's skills for a particular job.

Machine learned model framework for screening question generation

Techniques for scoring data items using a machine-learned model are provided. In one technique, multiple skills are identifying based on a job posting. Multiple attribute values of the job posting are identified. For each identified skill, multiple probabilities are identified, each probability corresponding to a different attribute value of the identified attribute values. The probabilities are input into a machine-learned model to generate multiple scores. Multiple skills of a candidate user are identified. An affinity score between the job posting and the candidate user is generated based the scores and the skills of the candidate user.

Generating a machine-learned model for scoring skills based on feedback from job posters

Techniques are provided for using machine learning techniques to analyze textual content. In one technique, a potential item is identified within a document. An analysis of the potential item is performed at multiple levels of granularity that includes two or more of a sentence level, a segment level, or a document level. The analysis produces multiple outputs, one for each level of granularity in the multiple levels of granularity. The outputs are input into a machine-learned model to generate a score for the potential item. Based on the score, the potential item is presented on a computing device. In response to user selection of the potential item, an association between the potential item and the document is created. The association may be used later to identify a set of users to which the document (or data thereof) is to be presented.

Machine learning techniques for analyzing textual content

During operation, the system obtains a first embedding produced by an embedding model from an input string representing an entity and a hierarchy of clusters of embeddings generated by the embedding model from a set of standardized entities. Next, the system searches the hierarchy of clusters for a subset of the embeddings that are within a threshold proximity to the first embedding in a vector space. The system then calculates embedding match scores between the input string and a first subset of the standardized entities represented by the subset of the embeddings based on distances between the subset of the embeddings and the first embedding in the vector space. Finally, the system modifies, based on the embedding match scores, content outputted in response to the input string within a user interface of an online system.

Semantic matching and retrieval of standardized entities

A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more computer processors and perform configuring one or more advertisements for a sponsored product customized to fit a page type associated with a page format of a website; analyzing one or more inputs to create merged data; generating a respective rank of the advertisements of the sponsored product using a conversion probability; adjusting the respective rank the one or more advertisements of the sponsored product as a function of the merged data; automatically designating one of the one or more advertisements of the sponsored product that is ranked higher than other advertisements to be positioned closest to a predefined portion of a webpage; and sending instructions to display the one of the one or more advertisements closest to the predefined portion of the webpage of the website. Other embodiments are disclosed.

System for page type based advertisement matching for sponsored product listings on e-commerce websites and method of using same

Relational graphs may be used to extract information. Similarities between the relational graphs and the items they represent may be determined. For example, when applied to video searching, relational graphs may be obtained from searching videos to extract objects, events and/or relations therebetween. Each relational graph may comprise a plurality of nodes and edges, wherein at least some of the detected objects and events are represented by each node, and wherein each edge and represents a relationship between two nodes. Subgraphs may be extracted from each relational graph and dimension reduction may be performed on the subgraphs to obtain a reduced variable set which may then be used to perform searches, such as similarity analyses of videos.

Graph matching by sub-graph grouping and indexing

A system and method includes receiving, by a computer system, one or more inputs of customer level information, advertisement page and page type level information, item level information, inventory constraints, competitor pricing, and seasonal requirements/rules, merging, by the computer system, the one or more inputs, receiving, by the computer system including a module selection and ranking engine, the merged data, and providing, by the computer system, output of one or more advertisements of sponsored product listings to a user device of a consumer for display.

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Los Gatos, CA, United States of America

Feng Guo