Abstract: Embodiments of the invention relate to methods and systems for evaluating entities for a target user, the method comprising obtaining, at a server computer, entity data from a plurality of data sources. The entity data is then stored in an entity database. The method further comprises merging the entity data from the plurality of data sources, mapping the entity data to a corresponding entity, and differentiating the entity. Then a relevance is determined associated with the entity data and data source. The method further comprises generating a set of entity evaluations to the target user using the relevance, determining a set of one or more entities relevant to the primary user based on the entity data, user data, and the relevance, with an initial order of relevance, and displaying, on a user device, the set of relevant entities to the target user in the order of relevance.

Abstract: Embodiments of the invention relate to a computer-implemented method and system for generating personalized recommendations for a target user based at least on stored data about the target user. The method comprises obtaining, at the server computer, data from a plurality of data sources, including entity data associated with a plurality of entities, stored in an entity database, or personal data associated with a plurality of users, stored in a user database. The personalized recommendations system then merges the entity data or personal data and maps the entity or personal data to a corresponding entity or target user, respectively. The entity or personal data is differentiated, a relevance is determined, a weight is assigned to the data and corresponding source to canonicalize the data, the respective databases are updated with the corresponding data, and then a set of personalized recommendations to the target user is generated using the updated databases.

Abstract: Embodiments of the invention relate to a computer-implemented method and system for generating personalized recommendations for a target user based at least on stored data about the target user. The method comprises obtaining, at the server computer, data from a plurality of data sources, including entity data associated with a plurality of entities, stored in an entity database, or personal data associated with a plurality of users, stored in a user database. The personalized recommendations system then merges the entity data or personal data and maps the entity or personal data to a corresponding entity or target user, respectively. The entity or personal data is differentiated, a relevance is determined, a weight is assigned to the data and corresponding source to canonicalize the data, the respective databases are updated with the corresponding data, and then a set of personalized recommendations to the target user is generated using the updated databases.

Abstract: Embodiments of the invention relate to methods and systems for evaluating entities for a target user, the method comprising obtaining, at a server computer, entity data from a plurality of data sources. The entity data is then stored in an entity database. The method further comprises merging the entity data from the plurality of data sources, mapping the entity data to a corresponding entity, and differentiating the entity. Then a relevance is determined associated with the entity data and data source. The method further comprises generating a set of entity evaluations to the target user using the relevance, determining a set of one or more entities relevant to the primary user based on the entity data, user data, and the relevance, with an initial order of relevance, and displaying, on a user device, the set of relevant entities to the target user in the order of relevance.

Abstract: Embodiments of the invention relate to methods and systems for evaluating entities for a target user, the method comprising obtaining, at a server computer, entity data from a plurality of data sources. The entity data is then stored in an entity database. The method further comprises merging the entity data from the plurality of data sources, mapping the entity data to a corresponding entity, and differentiating the entity. Then a relevance is determined associated with the entity data and data source. The method further comprises generating a set of entity evaluations to the target user using the relevance, determining a set of one or more entities relevant to the primary user based on the entity data, user data, and the relevance, with an initial order of relevance, and displaying, on a user device, the set of relevant entities to the target user in the order of relevance.

Abstract: Techniques for context-based queryless presentation of items are described. Without receiving an explicit query, an inferred query is determined based on a first set of context signals associated with a triggering event. Items matching the inferred query are selected based on a second set of one or more context signals associated with the triggering event. One or more results items identifying the one or more matching items are caused to display. Result items may be displayed in association with an explanation that identifies context values used to select an item or one or more attributes of the item that are relevant to a user or the inferred query.

Abstract: Techniques for context-based queryless presentation of items are described. Without receiving an explicit query, an inferred query is determined based on a first set of context signals associated with a triggering event. Items matching the inferred query are selected based on a second set of one or more context signals associated with the triggering event. One or more results items identifying the one or more matching items are caused to display. Result items may be displayed in association with an explanation that identifies context values used to select an item or one or more attributes of the item that are relevant to a user or the inferred query.

Abstract: Techniques for context-based queryless presentation of items are described. Without receiving an explicit query, an inferred query is determined based on a first set of context signals associated with a triggering event. Items matching the inferred query are selected based on a second set of one or more context signals associated with the triggering event. One or more results items identifying the one or more matching items are caused to display. Result items may be displayed in association with an explanation that identifies context values used to select an item or one or more attributes of the item that are relevant to a user or the inferred query.

Abstract: Embodiments of the invention relate to a computer-implemented method and system for generating personalized recommendations for a target user based at least on stored data about the target user. The method comprises obtaining, at the server computer, data from a plurality of data sources, including entity data associated with a plurality of entities, stored in an entity database, or personal data associated with a plurality of users, stored in a user database. The personalized recommendations system then merges the entity data or personal data and maps the entity or personal data to a corresponding entity or target user, respectively. The entity or personal data is differentiated, a relevance is determined, a weight is assigned to the data and corresponding source to canonicalize the data, the respective databases are updated with the corresponding data, and then a set of personalized recommendations to the target user is generated using the updated databases.

Abstract: Techniques for context-based queryless presentation of items are described. Without receiving an explicit query, an inferred query is determined based on a first set of context signals associated with a triggering event. Items matching the inferred query are selected based on a second set of one or more context signals associated with the triggering event. One or more results items identifying the one or more matching items are caused to display. Result items may be displayed in association with an explanation that identifies context values used to select an item or one or more attributes of the item that are relevant to a user or the inferred query.

Abstract: Techniques for context-based queryless presentation of recommendations are described. Without receiving an explicit query, an inferred query is determined based on a first set of context signals associated with a recommendation triggering event. Items matching the inferred query are selected based on a second set of one or more context signals associated with the recommendation triggering event. One or more results items identifying the one or more matching items are caused to display. A result items may be displayed in association with an explanation that identifies context values used to select an item for recommendation or one or more attributes of the item that are relevant to a user or the inferred query.

Abstract: Embodiments of the invention relate to methods and systems for evaluating entities for a target user, the method comprising obtaining, at a server computer, entity data from a plurality of data sources. The entity data is then stored in an entity database. The method further comprises merging the entity data from the plurality of data sources, mapping the entity data to a corresponding entity, and differentiating the entity. Then a relevance is determined associated with the entity data and data source. The method further comprises generating a set of entity evaluations to the target user using the relevance, determining a set of one or more entities relevant to the primary user based on the entity data, user data, and the relevance, with an initial order of relevance, and displaying, on a user device, the set of relevant entities to the target user in the order of relevance.

Abstract: Embodiments of the invention relate to a category based navigation system obtaining user data related to a plurality of users relevant to the primary user. The method further comprises obtaining entity data associated with an entity in a plurality of entities. The category based navigation system then determines one or more entities relevant to the primary user, and determines an initial order of relevance of a set of relevant entities. The method further comprises categorizing and displaying the set of relevant entities with an initial categorization on a user device to the primary user. The category based navigation system may then obtain, via the user device, user feedback, adjust the initial categorization and initial order of relevance based on the user feedback; and display the adjusted categorization and adjusted order of relevance of the set of relevant entities to the primary user on the user device.

Abstract: A recommendation engine provides a plurality of users with a recommendation for a common group action. The recommendation engine can take into user information such as explicit or implicit preferences of the participant of the group action in recommending a group action, such as shopping together, buying together, going to an event together, engaging in an activity together, etc. By considering the group, as well as the individuals, the recommendation engine can make recommendations optimized for a goal of one or more of the individuals, or for a group goal, which might not have been a recommendation made for any one of the individuals if considered in isolation. Additionally, in certain embodiments, the recommendation engine recommends an item related to a planned group activity for a group including a plurality of individuals based on information relating to each of the plurality of individuals.

Abstract: Techniques for context-based queryless presentation of recommendations are described. Without receiving an explicit query, an inferred query is determined based on a first set of context signals associated with a recommendation triggering event. Items matching the inferred query are selected based on a second set of one or more context signals associated with the recommendation triggering event. One or more results items identifying the one or more matching items are caused to display. A result items may be displayed in association with an explanation that identifies context values used to select an item for recommendation or one or more attributes of the item that are relevant to a user or the inferred query.

Abstract: Embodiments of the invention relate to a computer-implemented method for generating explanatory data from a personalized recommendations process for a primary user based at least on stored data about the primary user. The method comprises a server computer obtaining data related to one or more users who are relevant to the primary user, then determining at least one group of users relevant to the primary user. The server computer also obtains data related to one or more entities, determines one or more entities relevant to the primary user, and associates the at least one relevant group of users with the one or more relevant entities. One or more potential candidate factors are generated. A set of factors are selected from the one or more potential candidate factors, wherein the potential candidate factors are used as explanatory data to determine recommendations to the primary user.

Abstract: Embodiment of the invention relate to a computer-implemented method for providing augmented searches for entities to a user, the method comprising obtaining, at a server computer operated by an entity search system, user data related to a user, and obtaining entity data related to a plurality of entities. Then, the entity search system receives a query for a search for relevant entities from the user. The method then further comprises searching for a set of relevant entities based on the query, entity data, and user data, determining a set of criteria for an initial order of relevance, and determining an order of relevance of the set of relevant entities based on the set of criteria. The set of relevant entities or order of relevance may be augmented by obtaining feedback data from the user, thereby providing an augmented search for relevant entities to the user.

Abstract: Embodiments of the invention relate to a category based navigation system obtaining user data related to a plurality of users relevant to the primary user. The method further comprises obtaining entity data associated with an entity in a plurality of entities. The category based navigation system then determines one or more entities relevant to the primary user, and determines an initial order of relevance of a set of relevant entities. The method further comprises categorizing and displaying the set of relevant entities with an initial categorization on a user device to the primary user. The category based navigation system may then obtain, via the user device, user feedback, adjust the initial categorization and initial order of relevance based on the user feedback; and display the adjusted categorization and adjusted order of relevance of the set of relevant entities to the primary user on the user device.

Abstract: Embodiments of the invention relate to methods and systems for evaluating entities for a target user, the method comprising obtaining, at a server computer, entity data from a plurality of data sources. The entity data is then stored in an entity database. The method further comprises merging the entity data from the plurality of data sources, mapping the entity data to a corresponding entity, and differentiating the entity. Then a relevance is determined associated with the entity data and data source. The method further comprises generating a set of entity evaluations to the target user using the relevance, determining a set of one or more entities relevant to the primary user based on the entity data, user data, and the relevance, with an initial order of relevance, and displaying, on a user device, the set of relevant entities to the target user in the order of relevance.

Abstract: Embodiments of the invention relate to a computer-implemented method and system for generating personalized recommendations for a target user based at least on stored data about the target user. The method comprises obtaining, at the server computer, data from a plurality of data sources, including entity data associated with a plurality of entities, stored in an entity database, or personal data associated with a plurality of users, stored in a user database. The personalized recommendations system then merges the entity data or personal data and maps the entity or personal data to a corresponding entity or target user, respectively. The entity or personal data is differentiated, a relevance is determined, a weight is assigned to the data and corresponding source to canonicalize the data, the respective databases are updated with the corresponding data, and then a set of personalized recommendations to the target user is generated using the updated databases.