Abstract: Described herein is a technique for generating personalized scores for a cohort of users of an online service, where the scores are for use in ranking connection recommendations, in the context of generating connection recommendations for a user of the online service. The technique involves using a linear programming (LP) problem solver to solve a multi-objective optimization problem formulated to incorporate competing objectives and specific constraints. The technique allows for personalizing recommendations scores, specifically, to ensure that infrequent users are receiving invitations to connect with other users, thereby increasing overall interaction and engagement.

Abstract: Techniques are provided for automatically tuning a parameter in a layered model framework. One or more machine learning techniques are used to train multiple versions of a first model that includes a first version and a second version. A second model is stored that includes a parameter and accepts, as input, output from the first model. Multiple parameter values of the parameter are tested when processing content requests using the first and second versions of the first model. A strict subset of the plurality of parameter values are selected for the parameter of the second model, such that processing a first subset of the content requests using the first version of the first model results in a first value of a particular metric that matches a second value of the particular metric resulting from processing a second subset of the content requests using the second version of the first model.

Abstract: Described herein is a contextual contact recommendation or suggestion service and system. The service, which, in some embodiments, is integrated with a social networking service and/or an instant messaging service, takes as input a first parameter that identifies a member of the social networking service, and a second parameter that defines a context (e.g., a web page that is being viewed by the member. The service, based in part on the context, computes a ranked list of members to populate a contextual contact list, thereby recommending or suggesting contacts, with whom the member might be interested in initiating, or continuing, a conversation, based on the context of the member's current web browsing session. Optionally, the service may take as input a third parameter, defining a use case, such that the recommendation algorithm can be customized by use case.

Abstract: Systems and methods determine optimized hyperparameter values for one or more machine-learning models. A sample training data set from a larger corpus of training data is obtained. Initial hyperparameter values are then randomly selected. Using the sample training data set and the randomly chosen hyperparameter values, an initial set of performance metric values are obtained. Maximized hyperparameter values are then determined from the initial set of hyperparameter values based on the corresponding performance metric value. A larger corpus of training data is then evaluated using the maximized hyperparameter values and the corresponding machine-learning model, which yields another corresponding set of performance metric values. The maximized hyperparameter values and their corresponding set of performance metric values are then merged with the prior set of hyperparameter values.

Abstract: A system, a machine-readable storage medium storing instructions, and a computer-implemented method are described herein are directed to a Content Optimization Engine that determines a display probability for each content item in a set of content items. Each respective display probability corresponds to a given content item's probability of display in a specific content slot of a plurality of content slots in a social network feed of a target member account in a social network service. The Content Optimization Engine calculates a selection probability for each content item in an ordered set of the content items, based on each display probability and a set of interaction effects. The Content Optimization Engine causes display of the ordered set of content items in the target member account's social network feed based on satisfaction of the first and second targets.

Abstract: Techniques for tuning model parameters to optimize online content are disclosed herein. In some embodiments, a computer system receives logged data for cohorts of users, where the logged data of each one of the plurality of cohorts comprises a number of impressions of online content to the cohort, parameter values applied to objective functions of a model used in selecting the online content for the impressions, contribution actions by the cohort directed towards the online content, and clicks by the cohort directed towards the online content. The computer system, for each cohort, selects one of the parameter values for each objective function based on the logged data. The computer system then selects at least one content item for display to a target user based on the model using the parameter values corresponding to the cohort of the target user.

Abstract: Techniques are provided for automatically tuning a parameter in a layered model framework. One or more machine learning techniques are used to train multiple versions of a first model that includes a first version and a second version. A second model is stored that includes a parameter and accepts, as input, output from the first model. Multiple parameter values of the parameter are tested when processing content requests using the first and second versions of the first model. A strict subset of the plurality of parameter values are selected for the parameter of the second model, such that processing a first subset of the content requests using the first version of the first model results in a first value of a particular metric that matches a second value of the particular metric resulting from processing a second subset of the content requests using the second version of the first model.

Abstract: A unified notification platform for offline creation and distribution of notification content from a variety of data sources is described. The notification platform provides data adaptors that are reusable for generating notifications of different types, specifically, for generating notifications of different types that have features that have meaning across different notification types such that these features can be used to generate comparable relevance scores with respect to candidate profiles. The relevance score calculated for a notification with respect to a member profile is used to determine whether the notification is to be presented to the member represented by the member profile.

Abstract: In an example embodiment, training data is obtained, the training data comprising values for a plurality of different features. Then a global machine learned model is trained using a first machine learning algorithm by feeding the training data into the first machine learning algorithm during a fixed effect training process. A non-linear first random effects machine learned model is trained by feeding a subset of the training data into a second machine learning algorithm, the subset of the training data being limited to training data corresponding to a particular value of one of the plurality of different features.

Abstract: Technologies for optimizing content delivery to end-users are provided. Disclosed techniques include storing results of an online experiment with respect to a set of users and determining a plurality of distinct subsets of users based upon the results of the experiment. Users within each of the plurality of distinct subsets may be identified based on metric impacts of the online experiment. For each distinct subset and each associated model parameter, a utility value that represents effectiveness of the model parameter, with respect to an objective, may be determined. An objective optimization model may be used to automatically determine probabilities for each of the model parameters associated with each distinct subset. Users of a second set of users may be assigned to a distinct subset and associated model parameters may be applied to a content delivery strategies of the second set of users.

Abstract: Techniques for automatically identifying a primary objective for a multi-objective optimization problem are provided. In one technique, an experiment is conduct and results of the experiment involving different values of a model parameter are tracked and stored. Multiple metrics are generated based on the results. For each metric, a maximum or minimum value of the metric given a particular value of the model parameter is determined and a variance associated with the metric is determined based on the maximum or minimum value. A metric that is associated with the lowest variance among the multiple metrics is identified. The identified metric is used as a primary metric in a multi-objective optimization problem.

Abstract: Systems and methods determine optimized hyperparameter values for one or more machine-learning models. A sample training data set from a larger corpus of training data is obtained. Initial hyperparameter values are then randomly selected. Using the sample training data set and the randomly chosen hyperparameter values, an initial set of performance metric values are obtained. Maximized hyperparameter values are then determined from the initial set of hyperparameter values based on the corresponding performance metric value. A larger corpus of training data is then evaluated using the maximized hyperparameter values and the corresponding machine-learning model, which yields another corresponding set of performance metric values. The maximized hyperparameter values and their corresponding set of performance metric values are then merged with the prior set of hyperparameter values.

Abstract: Techniques for improving the accuracy, scalability, and efficiency of machine-learning models for selecting digital content items for display within a graphical user interface are disclosed herein.

Abstract: Techniques for minimizing variance in the estimation of the effects of a treatment on an online network are disclosed herein.

Abstract: A system, a machine-readable storage medium storing instructions, and a computer-implemented method are described herein are directed to Large Scale Optimizing Engine. The Large Scale Optimizing Engine determines a probability, for each content item in a set of content items, of the respective member account performing a content item action. The Large Scale Optimizing Engine identifies a select content item from the set of content items based on determining display of the select content item will meet a first and second target. The Large Scale Optimizing Engine causes display of the select content item in a content slot in the respective member account's social network feed based on satisfaction of the first and second targets.

Abstract: A unified notification platform for offline creation and distribution of notification content from a variety of data sources is described. The notification platform provides data adaptors that are reusable for generating notifications of different types, specifically, for generating notifications of different types that have features that have meaning across different notification types such that these features can be used to generate comparable relevance scores with respect to candidate profiles. The relevance score calculated for a notification with respect to a member profile is used to determine whether the notification is to be presented to the member represented by the member profile.

Abstract: A method may include determining a decision space representing a set of content items to be presented on a user interface of a social networking site, the decision space accounting for competing quadratic constraints and interaction effects, estimating the decision space to linearize the competing quadratic constraints, determining, in the estimated decision space and using an objective function, a display probability for each content item in the set of content items, each respective display probability corresponding to a given content item's probability of display in a specific content slot of a plurality of content slots on the user interface; and causing display of the content items with the highest display probabilities.

Abstract: Methods, systems, and computer programs are presented for identifying tuning parameters for mixing items in different categories for a user feed. One method includes maximizing utilities when presenting feeds to social network users, each utility having a weight for mixing items. The method further includes identifying a utilities maximization goal such that a first utility is maximized while other utilities are above a threshold, and initializing a counter. A loop, repeated until convergence, includes generating sample weights; performing an experiment with the sample weights for i users and j feed sessions to determine utility action indicators; for each utility, estimating a posterior distribution of an underlying hyperfunction and drawing samples; for each drawn sample, calculating a utility function and the weight that maximizes the utility function; generating an empirical distribution based on the sample weights; and incrementing the counter. The identified weights are utilized for creating the feeds.

Abstract: A system, a machine-readable storage medium storing instructions, and a computer-implemented method are described herein are directed to Large Scale Optimizing Engine. The Large Scale Optimizing Engine determines a probability, for each content item in a set of content items, of the respective member account performing a content item action. The Large Scale Optimizing Engine identifies a select content item from the set of content items based on determining display of the select content item will meet a first and second target. The Large Scale Optimizing Engine causes display of the select content item in a content slot in the respective member account's social network feed based on satisfaction of the first and second targets.

Abstract: Disclosed in some examples are methods, systems, and machine-readable mediums for matching mentee members with mentor members. The member matching may utilize social networking service data and one or more preferences of both the potential mentees and potential mentors. For example, after indicating an interest in being mentored (e.g., being a mentee), a member may be presented with a list of potential mentors that are selected, scored, and in some examples, ranked based upon the member's preferences, the potential mentors' preferences, and other compatibility factors. The member may then select one or more of these potential mentors.