Microsoft Technology Licensing, LLC

Jianling Zhong

Huiji Gao

Lin Guo

Bo Long

Haishan Liu

Weiwei Guo

Machine learning based method for generating personalized query suggestions is described. Different users may have different search intent even when they are inputting the same search query. The technical problem of personalizing search query suggestions produced by a machine learning model is addressed by extending the sequence to sequence machine learning model framework to be able to take into consideration additional, personalized features of the user, such as, e.g., profile industry, language, geographic location, etc. This methodology includes an offline model training framework as well as an online serving framework.

Generating personalized query suggestions

A neural related query generation approach in a search system uses a neural encoder that reads through a source query to build a query intent vector. The approach then processes the query intent vector through a neural decoder to emit a related query. By doing so, the approach gathers information from the entire source query before generating the related query. As a result, the neural encoder-decoder approach captures long-range dependencies in the source query such as, for example, structural ordering of query keywords. The approach can be used to generate related queries for long-tail source queries, including long-tail source queries never before or not recently submitted to the search system.

Neural related search query generation

Techniques for extracting features of entities and targets that can be applied in a set of applications, such as entity selection prediction, audience expansion, feed relevance, and job recommendation. In one technique, entity interaction data is stored that indicates, for each of multiple entities, one or more targets that are associated with items with which the entity interacted. Token association data is stored that indicates, for each of multiple tokens, one or more targets that are associated with the token. Then, using one or more machine learning techniques, entity embeddings and target embeddings are generated based on the entity interaction data and the token association data. Later, a request for content is received from a particular entity. Based on at least one entity embedding, a content item for the particular entity is identified. The content item is transferred over a computer network and presented to the particular entity.

Generating machine-learned entity embeddings based on online interactions and semantic context

Herein are techniques to use an artificial neural network to score the relevance of content items for a target and techniques to rank the content items based on their scores. In embodiments, a computer uses a plurality of expansion techniques to identify expanded targets for a content item. For each of the expanded targets, the computer provides inputs to an artificial neural network to generate a relevance score that indicates a relative suitability of the content item for that target. The computer ranks the expanded targets based on the relevance score generated for each of the expanded targets. Based on the ranking, the computer selects a subset of targets from the available expanded targets as the expanded targets for whom the content item is potentially most relevant. The computer stores an association between the content item and each target in the subset of expanded targets.

Growing community of inventors

San Jose, CA, United States of America

Jianling Zhong