Abstract: Embodiments of the disclosed technologies include generating a reward score for an entity. A rate distribution is determined using the reward score. A sampled rate value is generated by sampling the rate distribution. A probability score is generated for a pair of the entity and a user using the sampled rate value. A probability distribution is determined using the probability score. A sampled probability value is generated by sampling the probability distribution. A machine learning model is trained using the sampled probability value.

Abstract: Embodiments of the disclosed technologies include generating a reward score for an entity. A rate distribution is determined using the reward score and a number of times the entity has been selected for ranking. A sampled rate value is generated by sampling the rate distribution. A probability score is generated for a pair of the entity and a user based on the sampled rate value. A probability distribution is determined using the probability score. A sampled probability value is generated by sampling the probability distribution. A machine learning model is trained using the sampled probability value.

Abstract: In an example embodiment, a deep learning model is used to learn embedding representations of a heterogeneous information network, where the embedding represents entity-specific properties and network environment properties. Position-aware embeddings specific to the heterogeneous information network may be used as input features of the deep learning model. Furthermore, meta-path embedding specific to the heterogeneous information network may also be used as input features of the deep learning model. Modified embedding propagation methods are further designed to explore better ways to capture network meta-path properties.

Abstract: In an example embodiment, a deep learning model is used to learn embedding representations of a heterogeneous information network, where the embedding represents entity-specific properties and network environment properties. Position-aware embeddings specific to the heterogeneous information network may be used as input features of the deep learning model. Furthermore, meta-path embedding specific to the heterogeneous information network may also be used as input features of the deep learning model. Modified embedding propagation methods are further designed to explore better ways to capture network meta-path properties.

Abstract: In an example embodiment, a single machine learned model that allows for ranking of entities across all of the different combinations of node types and edge types is provided. The solution calibrates the scores from Edge-FPR models to a single scale. Additionally, the solution may utilize a per-edge type multiplicative factor dictated by the true importance of an edge type, which is learned through a counterfactual experimentation process. The solution may additionally optimize on a single, common downstream metric, specifically downstream interactions that can be compared against each other across all combinations of node types and edge types.

Abstract: Wireless communications and/or systems (e.g., 100) and/or methods (e.g., 200, 300, 400) may be provided for predicting of potential undetected flows in a DPI system using a machine learning (ML) model. The system may include an input packet module which may be configured for verifying packet parameters from a network traffic flow, and a processor which can be configured for processing the extracted parameters to identify whether the network traffic flow is potentially detectable or undetectable using a trained machine learning (ML) model based on at least the extracted parameters and perform DPI processing for the detectable flows. Thus, the system may provide an optimized DPI flow processing for high rate traffic networks with decreasing processing time.

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: Methods, systems, and computer programs are presented for providing a user experience that facilitates navigation among different topics and articles on a social network. One method includes an operation for identifying a hierarchy of topics, each topic corresponding to a respective subject, where the hierarchy defines relationships between the topics. A first topic page for a first topic is presented in a user interface in the social network. The first topic page includes articles and first options for navigating to topic pages of topics related to the first topic. The method further includes detecting a selection of a first article. In response to detecting the selection, a first article page for the first article is presented in the user interface. The first article page includes details of the first article and second options for navigating to topic pages of topics related to the first article.

Abstract: Techniques for generating training data to capture entity-to-entity affinities are provided. In one technique, first interaction data is stored that indicates interactions, that occurred during a first time period, between a first set of users and content items associated with a first set of entities. Also, second interaction data is stored that indicates interactions, that occurred during a second time period, between a second set of users and content items associated with a second set of entities. For each interaction in the first interaction data: (1) a training instance is generated; (2) it is determined whether the interaction matches one in the second interaction data; and (3) if the interaction does not match, then a negative label is generated for the training instance, else a positive label is generated for the training instance. Machine learning techniques are then used to train a machine-learned model based on the generating training instances.

Abstract: A system, a machine-readable storage medium storing instructions, and a computer-implemented method as described herein are directed to a Personalized Article Engine that generates respective prediction models for each article in a plurality of candidate articles in a social network system. The Personalized. Article Engine generates a respective article score according to each article's prediction model and at least one feature of a target member account. The Personalized Article Engine generates a plurality of output scores based on combining each respective article score with a corresponding article's global model score. The Personalized Article Engine ranks the output scores to identify a subset of candidate articles relevant to the target member account.

Abstract: In an example embodiment, a single machine learned model that allows for ranking of entities across all of the different combinations of node types and edge types is provided. The solution calibrates the scores from Edge-FPR models to a single scale. Additionally, the solution may utilize a per-edge type multiplicative factor dictated by the true importance of an edge type, which is learned through a counterfactual experimentation process. The solution may additionally optimize on a single, common downstream metric, specifically downstream interactions that can be compared against each other across all combinations of node types and edge types.

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: A system, a machine-readable storage medium storing instructions, and a computer-implemented method as described herein are directed to a Fast Ranker Engine that identifies global model features present in an article in a social network service. The Fast Ranker Engine assembles respective fixed vectors based on at least one member account feature and each coefficient that corresponds to a present global article feature of the global model. The Fast Ranker Engine generates a transformation feature(s) for a prediction model of the article based on the respective fixed vectors.

Abstract: Machine learning techniques for shaping downstream content traffic through hashtag suggestion during content creation are provided. In one technique, content item interaction data is stored that indicates, for each of multiple content items that is associated with one or more hashtags, whether a viewer interacted with the content item. Based on the content item interaction data, multiple training instances are generated, each corresponding to a different hashtag. One or more machine learning techniques are used to train a machine-learned downstream interaction model based on the training instances. Based on a particular content item, multiple candidate hashtags are identified. The machine-learned downstream interaction model is used to generate a score for each of the candidate hashtags. A subset of the candidate hashtags is selected based on the scores generated. The subset of the candidate hashtags are caused to be presented on a computing device.

Abstract: Methods, systems, and computer programs are presented for removing bias among users of an online service based on the amount of user's participation in the online service. One method includes operation for pre-training an invite model that provides a first score associated with a user of an online service and for pre-training an adversarial model that provides a second score, the adversarial model having the first score as an input. Further, the method includes training together the invite model and the adversarial model using an adversarial cost function based on the pre-training of the invite model and the adversarial model. The training together is repeated until discrimination of the invite model is below a predetermined threshold. Further, the invite model is utilized to generate the first scores, where the invite model generates the first scores without bias.

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 personalizing a user experience for a user of an online service using machine learning are disclosed herein. In some embodiments, a computer system detects a first request by a first computing device of a first user to access content of an online service, identifies at least one content item to display based on the first request, and selects a first presentation template from amongst a plurality of presentation templates based on the at least one content item and an identification of the first user. In some example embodiments, the plurality of presentation templates is stored in a database of the online service, and each one of the plurality of presentation templates is distinct from one another and defines a corresponding manner in which to display the at least one content item.

Abstract: The present disclosure describes various embodiments of methods, systems, and machine-readable mediums which help determine a user's likely affinity for consuming content (such as an article) in a particular language presented (or to be presented) in a heterogeneous feed of a social network.

Abstract: The present disclosure describes various embodiments of methods, systems, and machine-readable mediums which help determine a user's likely affinity for an article presented (or to be presented) in a heterogeneous feed of a social network.

Abstract: A computer-implemented method may determine content items regarding a subject to be high demand and sufficient supply, low demand and supply constrained, high demand and supply constrained, or low demand and supply constrained. The computer-implemented method may determine the following: a supply and demand of content items regarding a subject for members, supply demand ratios for the content items regarding the subject for each of the plurality of members, a median supply demand ratio of the supply demand ratios, a total demand for the content items regarding the subject, a median total demand of total demands for the content items regarding subjects for the members, and a median of median supplies demand ratios for the content items regarding the subjects for the members. The method may perform steps to improve demand or supply of a connection network.