Microsoft Technology Licensing, LLC

Excalibur Ip, LLC

Nagaraj Kota

Venkatesh Duppada

Mohit Wadhwa

Nadeem Anjum

Amit Chandak

Vinayak Shukl

Ashvini Kumar Jindal

In an example embodiment an approximate nearest neighbor framework is provided to query user activity data to find users who are similar to users who have been 'matched' to a particular piece of content but who otherwise would not have been matched on their own. The users who have been matched may be called a seed set of users, which are known in real-time, or near-real-time. Use of the approximate nearest neighbor framework allows the system to expand instantly the initial seed set of users to other similar users to rapidly distribute relevant pieces of content to active users, increasing liquidity of the system. Additionally, the target set of specific users to which a notification is sent about the pieces of content can also be expanded, increasing the recall rate.

Instant content notification with user similarity

In an example embodiment, user interactions with a graphical user interface are modeled to derive an efficient representation that is highly available through a framework. This representation enables downstream analysis as to the relevancy of the user interactions through libraries leveraging standardized activity representations. With these components, it becomes possible to derive user intent in a modular fashion, domain by domain, while decoupling many system aspects, and also providing high capacity and precise intent information to leverage for personalization.

Unified intent understanding for deep personalization

In an example, a piece of content is obtained. The piece of content is segmented into a plurality of segments. Each of the plurality of segments is divided into a plurality of units. Then, for each of the plurality of units for each segment, a quality model is used to pass the unit through a long short-term memory (LSTM) corresponding to the unit, causing an embedding of the unit and generating one or more parameters. For each of the plurality of segments for the piece of content, the quality model is used to pass the segment, and one or more parameters obtained from LSTMs corresponding to units within the segment, through an LSTM corresponding to the segment, causing an embedding of the segment. The piece of content is then classified as spam based on the embeddings of the plurality of segments for the piece of content, using the quality model.

Multilevel representation learning for computer content quality

In some embodiments, a computer system detects user-entered text that has been entered in a search field of a search engine via a user interface of a computing device of a user, determines a context representation for the user-entered text based on one or more search queries submitted by the user within a particular amount of time before the user-entered text was entered, generates a corresponding score for each one of a plurality of auto-completion candidates based on the auto-completion candidate and the context representation, and causes at least a portion of the plurality of auto-completion candidates to be displayed in an auto-complete user interface element of the search field based on the corresponding scores of the at least a portion of the plurality of auto-completion candidates prior to the user-entered text being submitted by the user as part of a search query.

Context aware personalized query autocompletion

The disclosed embodiments provide a system for processing data. During operation, the system obtains a first sentence representing a first sequence of actions between a user and a set of jobs. Next, the system applies a language model to token embeddings of a first set of tokens in the first sentence and position embeddings of token positions in the first sentence to produce a first set of output embeddings. The system then combines the first set of output embeddings into a first session embedding that encodes the first sequence of actions. Finally, the system outputs the first session embedding for use in characterizing job-seeking activity of the user.

Session embeddings for summarizing activity

Systems and methods for rewriting queries based on data features are disclosed. A data source contains a history of queries and results associated with the queries. A source query is received and a subset of the data source is extracted that is related to the query. Costs are then associated with the associations of the queries and results, and an absorbed cost is determined for each query. The queries having the lowest absorbed cost are recommended for rewrites.

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Bengaluru, India

Nagaraj Kota