Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Disclosed are systems, methods, and non-transitory computer-readable media for guided query recommendations. A search system generates search query recommendations for a user based on activity data associated with the user. In one technique, the search system generates a search query recommendation based on a search query sequence identified from the activity data of the user. For example, the search query sequence is used as input into a machine learning model, such as a sequence to sequence model trained on historical search query sequences that resulted in a targeted action. In another technique, the search system generates a search query recommendation based on multi-session query data of the user. For example, the search system generates a multi-session embedding vector representing the multiple activity sessions of the user. The multi-session embedding vector is used as input in a classification model that assigns probability values to candidate search terms.

Abstract: 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.

Abstract: 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.