Abstract: An extended two tower network is used to make both a recommendation of a content item that a given user may be interested in, and a recommendation of a user that may be interested in a given content item. The extended two tower network includes a content item sub-tower, a user sub-tower, and a fusion sub-model that are jointly trained to predict a probability that a given user is interested in a given content item. The content item sub-tower and the user sub-tower are used to make an initial prediction that the given user will be interested in the given content item. The initial prediction is then input to the fusion sub-model to make a final prediction. In the case of a candidate invitee recommendation, the initial prediction may be combined with one or more interaction features and the combination input to the fusion sub-model to make the final prediction.

Abstract: Technologies for determining whether to send a notification to an entity is provided. Disclosed techniques include receiving entity features describing attributes related to observed entity sessions. A set of entity-specific session features values may be generated from the received entity features. A session-quality prediction model may be generated using the set of entity-specific session feature values. The session-quality prediction model may determine an expected session score for a new entity session for an entity, where the expected session score describes a level of interaction for the new entity session. A notification may be received for a particular entity. The session-quality prediction model may be used to determine the expected session score for a new entity session for the particular entity. A determination may be made as to whether a notification should be sent to the particular entity based upon the expected session score for the new entity session.

Abstract: Technologies for determining whether to send a notification to an entity is provided. Disclosed techniques include receiving entity features describing attributes related to observed entity sessions. A set of entity-specific session features values may be generated from the received entity features. A session-quality prediction model may be generated using the set of entity-specific session feature values. The session-quality prediction model may determine an expected session score for a new entity session for an entity, where the expected session score describes a level of interaction for the new entity session. A notification may be received for a particular entity. The session-quality prediction model may be used to determine the expected session score for a new entity session for the particular entity. A determination may be made as to whether a notification should be sent to the particular entity based upon the expected session score for the new entity session.

Abstract: A system for generating structured queries and carrying out searches using the structured queries may result in high quality queries and may adequately simulate natural language for suggested search queries. In embodiments, attributes and matching product/services may be extracted from various data sources. Structured queries that include the attributes, products/services, and/or additional terms may be combined to form a structured query. In various embodiments, each structured query may be structured according to a pre-defined pattern that is selected based on the type of relevant product and/or service.

Abstract: A system for generating structured queries and carrying out searches using the structured queries may result in high quality queries and may adequately simulate natural language for suggested search queries. In embodiments, attributes and matching product/services may be extracted from various data sources. Structured queries that include the attributes, products/services, and/or additional terms may be combined to form a structured query. In various embodiments, each structured query may be structured according to a pre-defined pattern that is selected based on the type of relevant product and/or service.