Abstract: A notification platform for distribution of notification content in an on-line social network system addresses the technical problem of optimizing the volume of quality notifications that are being delivered to a given member. A notification delivery system is designed as a stream processing system that can fetch, store, and process data in a near-line fashion. It can perform feature generation, processing and scoring of notifications, as well as ranking of the notifications based on their respective relevance scores that are calculated using machine learning techniques. The notification delivery system is positioned centrally with respect to different producers of notifications, such that it can consume centrally-stored information about members' holistic notification experiences.

Abstract: Techniques for reducing delay in broadcasting content over a network using an inverted fan-out process are disclosed herein. In some embodiments, a computer-implemented method comprises: in response to an activity associated with content being performed by a user on an online service, detecting that the activity has been performed: identifying a plurality of recipient users in response to the detecting; and for each one of the plurality of recipient users, transmitting a notification of the activity to a destination associated with the recipient user in response to the identifying of the recipient users, the notification comprising an indication of the content, and the transmitting of the notification of the activity being performed without waiting for the recipient user to navigate to a web page of the online service on a computing device or for the recipient to open a mobile application of the online service on a mobile device.

Abstract: A method can include determining, based on learned parameter values, an intrinsic interest and an affinity for the user to be influenced to visit the website, determining, using the learned parameter values, intrinsic interest, and affinity for the user to be influenced to visit the website, a first probability indicating a likelihood that the user will, in response to viewing a badge notification, turn off notifications or delete an app and a second probability indicating a likelihood that the user will, in response to viewing the badge notification on the app, visit a website, in response to determining the second probability is greater than a threshold larger than the first probability, causing the app to include the badge notification when displayed on the user device.

Abstract: A system and method for content generation and targeting using machine learning are provided. In example embodiments, a probability that a user will visit a webpage based on historical data is calculated. A probability that the user will engage with a particular content category based on past user engagement is calculated. In response to the probability of the user engaging with the particular content category being equal to or greater than a first threshold, the content is generated. Further, in response to the probability of the user not visiting a webpage meeting or exceeding a second threshold, the generated content is sent to the user.

Abstract: Methods and apparatus for a system providing group recommendations, event scheduling, and forming consensus agreements among a group of disparately located users are provided by the present principles. Communication among the disparately located users may be achieved through social media tools, online polling, chatting, and texting. The methods and apparatus allow a user profile to be constructed based on ratings that a user provides to items in a database. The items may comprise such things as restaurants, movie theaters, or other entertainment and group activities. The items may comprise feature vectors including attributes of the individual items. With user profiles constructed, the methods and apparatus may allow prediction of a rating that an individual with a particular user profile may give to a similar item in a database. The system may be used to predict the best choice for a group activity by considering the ratings of all users within a group.

Abstract: A method of selection that maximizes an expected reward in a contextual multi-armed bandit setting gathers rewards from randomly selected items in a database of items, where the items correspond to arms in a contextual multi-armed bandit setting. Initially, an item is selected at random and is transmitted to a user device which generates a reward. The items and resulting rewards are recorded. Subsequently, a context is generated by the user device which causes a learning and selection engine to calculate an estimate for each arm in the specific context, the estimate calculated using the recorded items and resulting rewards. Using the estimate, an item from the database is selected and transferred to the user device. The selected item is chosen to maximize a probability of a reward from the user device.

Abstract: A method for generating a recommendation item for members of a group includes registering a plurality of users as members of the group, identifying a subgroup of members of the group of users wherein the subgroup requests a recommendation item from a recommendation engine, calculating, using a multi-armed bandit algorithm, a recommendation item for the subgroup of members. The recommendation item is provided to the subgroup members for their evaluation. After evaluating the recommendation item, the individual users rate the recommendation item which updates the recommendation engine with preferences representing the members of the subgroup.