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公开(公告)号：US11475084B2
公开(公告)日：2022-10-18
申请号：US16454622
申请日：2019-06-27
发明人： Yan Gao , Ajith Muralidharan , Pratik Daga , Sandor Nyako , Nirav Nalinbhai Shingala , Matthew H. Walker
IPC分类号： G06N20/00 , G06F16/9535 , G06K9/62 , G06Q50/00
摘要： Technologies for generating dynamic notification content for notification messages using a machine learned model are provided. The disclosed techniques include identifying an event related to a particular user, where the event has a particular notification type that represents a subject type of the event. Based on the particular notification type of the event, a set of candidate headline and call-to-action combinations corresponding to the particular notification type are identified. Using the machine learned model, scores are calculated for each headline and call-to-action combination in the set of candidate headline and call-to-action combinations. One or more particular headline and call-to-action combinations from the set of candidate headline and call-to-action combinations are selected based upon the scores calculated for each combination of the set of candidate headline and call-to-action combinations. A notification message is generated for the event that includes the one or more particular headline and call-to-action combinations selected.
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公开(公告)号：US11606443B1
公开(公告)日：2023-03-14
申请号：US17559654
申请日：2021-12-22
发明人： David Benjamin Liu , Rakesh Malladi , Swetha Nagabhushan Karthik , Gargi Harish Bhandari , Ajith Muralidharan , Ruiqi Wang
IPC分类号： H04L67/55 , G06F9/54 , G06F16/2455 , G06K9/62
摘要： Technologies for unseen notification handling are described. Embodiments select an initial set of notifications, provide the selected initial set of notifications to a client device, store seen notifications in a first data store, maintain sent but unseen notifications in a second data store that is an in-memory online data store, retrieve a set of the sent but unseen notifications from the second data store, create a list of unseen notifications by combining the retrieved set of sent but unseen notifications with a set of unsent and unseen notifications, generate a set of relevance scores for the list of unseen notifications, create a new version of the list of unseen notifications based on the new set of relevance scores, and provide the new version of the list of the unseen notifications to the client device.
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公开(公告)号：US11556864B2
公开(公告)日：2023-01-17
申请号：US16674422
申请日：2019-11-05
发明人： Yiping Yuan , Ajith Muralidharan , Shaunak Chatterjee , Preetam Nandy , Shipeng Yu , Miao Cheng
摘要： Methods, systems, and computer programs are presented for scheduling user notifications to maximize short-term and long-term benefits from sending the notifications. One method includes an operation for identifying features of a state used for reinforcement learning. The state is associated with an action to decide if a notification to a user is to be sent and a reward for sending the notification to the user. Further, the method includes capturing user responses to notifications sent to users to obtain training data and training a machine-learning (ML) algorithm with reinforcement learning based on the features and the training data to obtain an ML model. Additionally, the method includes receiving a request to send a notification to the user, and deciding, by the ML model, whether to send the notification based on a current state. The notification is sent to the user based on the decision.
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公开(公告)号：US20210133642A1
公开(公告)日：2021-05-06
申请号：US16674422
申请日：2019-11-05
发明人： Yiping Yuan , Ajith Muralidharan , Shaunak Chatterjee , Preetam Nandy , Shipeng Yu , Miao Cheng
摘要： Methods, systems, and computer programs are presented for scheduling user notifications to maximize short-term and long-term benefits from sending the notifications. One method includes an operation for identifying features of a state used for reinforcement learning. The state is associated with an action to decide if a notification to a user is to be sent and a reward for sending the notification to the user. Further, the method includes capturing user responses to notifications sent to users to obtain training data and training a machine-learning (ML) algorithm with reinforcement learning based on the features and the training data to obtain an ML model. Additionally, the method includes receiving a request to send a notification to the user, and deciding, by the ML model, whether to send the notification based on a current state. The notification is sent to the user based on the decision.
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5.发明授权公开(公告)号：US10977096B1
公开(公告)日：2021-04-13
申请号：US16588232
申请日：2019-09-30
摘要： Technologies for determining whether to send notification messages, from different sources, to a target user are provided. The disclosed techniques include receiving a first notification event from a first notification service and receiving a second notification event from a second notification service. The first and second notification services are different services. Using a machine-learned model to assign a first score to the first notification event and a second score to the second notification event. Based on the first score, a determination is made to generate a first notification message for the first notification event. The first notification message is then sent to a target user. Based on the second score, a determination is made not to generate a second notification message for the second notification event.
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公开(公告)号：US11288591B2
公开(公告)日：2022-03-29
申请号：US15442069
申请日：2017-02-24
发明人： Ankan Saha , Ajith Muralidharan
IPC分类号： G06N20/00 , G06Q50/00 , G06N5/04 , G06F16/248 , G06F16/2458 , G06F16/25 , G16H70/60 , G06N5/02 , H04L67/306 , G16H80/00 , G16H10/60 , H04L67/10 , H04L67/01
摘要： 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.
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公开(公告)号：US20200252467A1
公开(公告)日：2020-08-06
申请号：US16264322
申请日：2019-01-31
发明人： Jiaqi Ge , Yiping Yuan , Ajith Muralidharan , Padmini Jaikumar , Shipeng Yu
IPC分类号： H04L29/08
摘要： Technologies for determining whether to send a notification to an entity is provided. Disclosed techniques include receiving entity features describing attributes related to observed entity sessions. A set of entity-specific session features values may be generated from the received entity features. A session-quality prediction model may be generated using the set of entity-specific session feature values. The session-quality prediction model may determine an expected session score for a new entity session for an entity, where the expected session score describes a level of interaction for the new entity session. A notification may be received for a particular entity. The session-quality prediction model may be used to determine the expected session score for a new entity session for the particular entity. A determination may be made as to whether a notification should be sent to the particular entity based upon the expected session score for the new entity session.
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公开(公告)号：US20190385089A1
公开(公告)日：2019-12-19
申请号：US16008190
申请日：2018-06-14
摘要： 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.
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公开(公告)号：US20240273282A1
公开(公告)日：2024-08-15
申请号：US18169808
申请日：2023-02-15
发明人： Ajith Muralidharan , Aastha Jain , Zhanglong Liu
IPC分类号： G06F40/166 , G06F40/30
CPC分类号： G06F40/166 , G06F40/30
摘要： Embodiments of the disclosed technologies include identifying a user of a network as a possible contributor of digital content to a document that is published via the network; identifying at least two different channels on the network that are each capable of sending, to the user, an invitation for the user to contribute to the document; for each of the at least two different channels, determining respective channel usage data, where the channel usage data includes, for a channel of the at least two different channels, historical data relating to use of the channel by the user to interact with content; for each of the at least two different channels, computing respective channel affinity scores based on the respective channel usage data, where a channel affinity score includes, for a channel of the at least two different channels, an estimate of a likelihood of the user contributing to the document through the channel; based on the respective channel affinity scores, selecting an optimal channel from the at least two different channels; and sending the invitation to the user to contribute to the document through the optimal channel.
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公开(公告)号：US11968165B1
公开(公告)日：2024-04-23
申请号：US18086138
申请日：2022-12-21
摘要： Methods, systems, and computer programs are presented for selecting notifications based on an affinity score between a content generator and a viewer of the content. One method includes capturing interactions of content generators with notifications, received by the content generators, associated with viewer responses to creator-generated content items. The method further includes training a machine-learning model based on the interactions, and detecting a first set of notifications, for a first content generator, associated with interactions of a set of viewers to first-content generator content. The ML model calculates an affinity score between the first content generator and each viewer, and the set of first notifications are ranked based on the affinity scores of the first content generator and the viewer associated with each notification. A set of second notifications is selected based on the ranked first notifications; and generating notifications are generated, for the first content generator, for the selected set of second notifications.
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