Abstract: In an example embodiment, a separate mimicry machine-learned model is trained for each of a plurality of different item types. Each of these models is trained to estimate an effect of mimicry for a user (i.e., a user whose user profile or other information is passed to the corresponding mimicry machine-learned model at prediction-time). The output of these models may be either used on its own to perform various actions, such as modifying a location of a user interface element of a user interface, or may be passed as input to an interaction machine-learned model that is trained to determine a likelihood of a user (i.e., a user whose user profile or other information is passed to the interaction machine-learned model at prediction-time) interacting with a particular item, such as a potential feed item.

Abstract: In an example embodiment, a user's session sequence data is utilized to provide a universal member representation that achieves one or more of the following goals: 1. Provides a user-level representation that enables the prediction of future actions based on historical interactions within different domains 2. Provides a user representation that allows better clarification of user intent (e.g., network builder, job seeker, profile scraper, etc.) 3. Members with similar/behaviors/intent are easily identified 4. Less sensitivity to activity levels of members.

Abstract: In an example embodiment, a deep machine learning model ranks cohorts of users as well as cohorts of products in a single ranking. When utilized to determine which cohort members to display to a user, the system selects one user cohort and one product cohort as the “best” (e.g., the top ranked user cohort and the top ranked product cohort). This ranking may be based on a number of contextual and non-contextual features, including viewer features (characteristics of the user operating the user interface), viewee features (characteristics of or related to the litem that the user is viewing, such as the characteristics of another user whose profile the user is viewing), and viewer-viewee relationship features (indications about how the viewer and viewee are related, such as common schools, locations, places of employment, etc.).

Abstract: In an example embodiment, a separate mimicry machine-learned model is trained for each of a plurality of different item types. Each of these models is trained to estimate an effect of mimicry for a user (i.e., a user whose user profile or other information is passed to the corresponding mimicry machine-learned model at prediction-time). The output of these models may be either used on its own to perform various actions, such as modifying a location of a user interface element of a user interface, or may be passed as input to an interaction machine-learned model that is trained to determine a likelihood of a user (i.e., a user whose user profile or other information is passed to the interaction machine-learned model at prediction-time) interacting with a particular item, such as a potential feed item.

Abstract: Techniques for nurturing content creation are provided. In one technique, a particular user is identified. Candidate entities are identified based on one or more attributes of the particular user. For each candidate entity, a feedback sensitivity measure of content creation of the candidate entity is determined. The feedback sensitivity measure is generated based on an amount of feedback, from other users, to content that the candidate entity has created. A score is then generated for the candidate entity based on the measure. A ranking of the candidate entities is determined based on the score of each candidate entity. A subset of the candidate entities is selected based on the ranking. The subset of the candidate entities is transmitted over a computer network to be presented on a computing device of the particular user.

Abstract: Techniques for nurturing content creation are provided. In one technique, a particular user is identified. Candidate entities are identified based on one or more attributes of the particular user. For each candidate entity, a feedback sensitivity measure of content creation of the candidate entity is determined. The feedback sensitivity measure is generated based on an amount of feedback, from other users, to content that the candidate entity has created. A score is then generated for the candidate entity based on the measure. A ranking of the candidate entities is determined based on the score of each candidate entity. A subset of the candidate entities is selected based on the ranking. The subset of the candidate entities is transmitted over a computer network to be presented on a computing device of the particular user.

Abstract: A machine for content-feedback-based machine learning to incent online content creation. The machine accesses a relevance value that identifies a level of relevance of a content item to a user. The content item is created by a content creator. The machine generates, using a machine learning model, a feedback sensitivity score associated with the content creator. The machine generates, based on the relevance value and a product between the feedback sensitivity score and a likelihood of the user providing a feedback signal in relation to the content item, a ranking score for the content item. The machine causes display of the content item, based on the ranking score, in a user interface of a client device associated with the user. An input pertaining to the content item received via the user interface causes improvement of the machine learning model based on updating the one or more feedback features.

Abstract: A machine for content-feedback-based machine learning to incent online content creation. The machine accesses a relevance value that identifies a level of relevance of a content item to a user. The content item is created by a content creator. The machine generates, using a machine learning model, a feedback sensitivity score associated with the content creator. The machine generates, based on the relevance value and a product between the feedback sensitivity score and a likelihood of the user providing a feedback signal in relation to the content item, a ranking score for the content item. The machine causes display of the content item, based on the ranking score, in a user interface of a client device associated with the user. An input pertaining to the content item received via the user interface causes improvement of the machine learning model based on updating the one or more feedback features.

Abstract: An automotive active remote encryption and switching device is revealed. Remote images and information are transmitted through wireless communication for monitoring vehicle status. The device includes at least one video mobile phone with specific permission, a control mainframe for receiving signals from the video mobile phone, a remote video recorder mounted in a vehicle, a detection unit that detects conditions inside the vehicle and a receiver that receives signals from the control mainframe. Thereby the remote images showing conditions in the vehicle are detected by the detection unit and sent to users by wireless communication equipments available now. Thus users can carry out corresponding measures and the active remote control of the vehicle is achieved by the video mobile phone.

Abstract: A home use active remote encryption and switching device is revealed. Transmission of remote images and information as well as switching of home appliances is controlled by wireless communication. The home use active remote encryption and switching device includes at least one video mobile phone with specific permission, a signal receiving interface receiving signals from the mobile phone, a digital signal processor that decrypts and modulates signals from the mobile phone, a central processing unit that integrates and converts decrypted signals into switching signals, and a storage device storing audio/video signals. Thereby signals from the video mobile phone are transmitted to the remote signal receiving interface by wireless communication equipments. Then the signals are decrypted by the digital signal processor, converted into switching signals by the central processing unit, and sent to home appliances. Thus remote switching of home appliances is achieved under the control of the video mobile phone.