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1.发明申请公开(公告)号：US20190005575A1
公开(公告)日：2019-01-03
申请号：US15640052
申请日：2017-06-30
Applicant: Facebook, Inc.
Inventor： Robert Oliver Burns Zeldin , Chinmay Deepak Karande , Shyamsundar Rajaram , Leon R. Cho , Rami Mahdi , Sushma Nagesh Bannur
Abstract: An online system calculates bids for content items to display to users based on the value of a product described in the content item and the likelihood of a viewing user purchasing the product. The online system identifies an impression opportunity for an ad request and computes an expected value of the conversion and a likelihood of the conversion. The online system computes a bid amount based on the expected conversion value and the likelihood of the conversion. Bids based on the value of the conversion allow a third party system offering the product to optimize for the value of each conversion instead of the conversion rate.
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公开(公告)号：US20180150874A1
公开(公告)日：2018-05-31
申请号：US15364999
申请日：2016-11-30
Applicant: Facebook, Inc.
Inventor： Zheng Chen , Shyamsundar Rajaram , Pradheep K. Elango
Abstract: An online system optimizes for longer attribution window conversions with an additive decomposition model by predicting the probability that a predefined action happens given an impression/click. The online system receives a content item from a content provider for display to a target user, and predicts a probability that a target user will convert given an interaction with the content item by the target user. The online system computes, by a first trained model, a short-term conversion probability of a conversion event happening within a first conversion window after the interaction. The online system computes, by a second trained model, a long-term conversion probability of the a conversion event happening within a second conversion window after the interaction, the second conversion window being longer than the first conversion window. The online system computes the conversion probability given the interaction based on the short-term conversion probability and the long-term conversion probability.
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公开(公告)号：US20170161779A1
公开(公告)日：2017-06-08
申请号：US14960988
申请日：2015-12-07
Applicant: Facebook, Inc.
Inventor： Stuart Michael Bowers , Shyamsundar Rajaram , Rubinder Singh Sethi
IPC: G06Q30/02
CPC classification number: G06Q30/0247 , G06Q30/0255 , G06Q30/0275 , G06Q30/0277
Abstract: An advertising platform calculates bids for advertisements and optimizes bids for a plurality of advertisement objectives, where each objective corresponds to a unique user action. The advertising platform identifies an impression opportunity for an advertisement request, computes a bid amount for presenting the advertisement, and provides the computed bid amount to an advertisement selection process. The bid amount is computed based on expected values of user actions associated with the plurality of advertisement objectives and an expected value multiplier of one or more advertisement objectives, where the expected value multiplier of the one or more objectives represents a bound on a range of values for the expected values of the user actions associated with the one or more objectives.
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公开(公告)号：US11157955B2
公开(公告)日：2021-10-26
申请号：US14662222
申请日：2015-03-18
Applicant: Facebook, Inc.
Inventor： Feng Yan , Shyamsundar Rajaram , Hao Zhang , Lu Zheng , Tianshi Gao , David Michael Viner
IPC: G06Q30/00 , G06Q30/02 , G05B19/418
Abstract: An online system tracks stores information identifying content provided by third party systems and accessed by online system users as well as interactions with advertisements performed by online system users. When the online system identifies an opportunity to present an advertisement to a viewing user, the online system identifies content from third party systems accessed by the viewing user and content from third party systems accessed by additional online system users who interacted with advertisements. A score is computed for various advertisements based at least in part on correlations between content from third party systems accessed by the viewing user and content from third party systems accessed by additional online system users who interacted with advertisements. The online system selects candidate advertisements to evaluate for presentation to the viewing user based on the scores.
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公开(公告)号：US20180336621A1
公开(公告)日：2018-11-22
申请号：US15600652
申请日：2017-05-19
Applicant: Facebook, Inc.
Inventor： Pradheep K. Elango , Shyamsundar Rajaram , Apurva Jadhav , Yanxi Pan , Shike Mei , Aashish Pant , Amit Madaan , Shashikant Khandelwal
Abstract: An online system generates a content item for a user based on products likely to be of interest to the user. The online system receives information about products associated one or more third party systems accessed by users of the online system. When the online system identifies an opportunity to present to a user, the online system identifies candidate products for inclusion in the content item based on products previously accessed by the users. For example, the online system identifies candidate products based on products accessed by the user and by one or more other users. The online system may include differing levels of information about a selected candidate product in the content item. In various embodiments, the online system determines a level of information about the selected candidate product based on products previously accessed by the user.
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Patent Agency Ranking
公开(公告)号：US20180336600A1
公开(公告)日：2018-11-22
申请号：US15600640
申请日：2017-05-19
Applicant: Facebook, Inc.
Inventor： Pradheep K. Elango , Shyamsundar Rajaram , Apurva Jadhav , Yanxi Pan , Shike Mei , Aashish Pant , Amit Madaan , Shashikant Khandelwal
IPC: G06Q30/02
Abstract: An online system generates a content item for a user based on products likely to be of interest to the user. The online system receives information about content provided by one or more third party systems accessed by the user and determines products associated with accessed content. When the online system identifies an opportunity to present to a user, the online system identifies products for inclusion in the content item and identifies candidate products for inclusion in the content item based on products previously accessed by the user. The online system selects a product of the candidate products based on probabilities of the user accessing content items including different candidate products. The online system includes the content item having information about the selected product in one or more selection processes that select content for presentation to the user.
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公开(公告)号：US11062360B1
公开(公告)日：2021-07-13
申请号：US15899581
申请日：2018-02-20
Applicant: Facebook, Inc.
Inventor： Raghavendra Rao Donamukkala , Zheng Chen , Toby Jonas F Roessingh , Shyamsundar Rajaram , Leon R Cho
Abstract: The present disclosure is directed toward systems and methods for optimizing view-through conversion rates. For example, systems and methods described herein train and utilize a machine learning model that predicts whether providing a digital impression to a particular networking system user will result in a conversion. Systems and methods described herein identify view-through conversions by generating a vector associated with the provision of a digital impression to a networking system user and receiving third-party conversion information during an attribution window. The systems and methods described herein then utilize the vector and conversion information to train the machine learning model to predict future conversions.
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公开(公告)号：US10664866B2
公开(公告)日：2020-05-26
申请号：US15364999
申请日：2016-11-30
Applicant: Facebook, Inc.
Inventor： Zheng Chen , Shyamsundar Rajaram , Pradheep K. Elango
Abstract: An online system optimizes for longer attribution window conversions with an additive decomposition model by predicting the probability that a predefined action happens given an impression/click. The online system receives a content item from a content provider for display to a target user, and predicts a probability that a target user will convert given an interaction with the content item by the target user. The online system computes, by a first trained model, a short-term conversion probability of a conversion event happening within a first conversion window after the interaction. The online system computes, by a second trained model, a long-term conversion probability of the a conversion event happening within a second conversion window after the interaction, the second conversion window being longer than the first conversion window. The online system computes the conversion probability given the interaction based on the short-term conversion probability and the long-term conversion probability.
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公开(公告)号：US10567235B1
公开(公告)日：2020-02-18
申请号：US15899848
申请日：2018-02-20
Applicant: Facebook, Inc.
Inventor： Nimish Rameshbhai Shah , Raghavendra Rao Donamukkala , Chinmay Deepak Karande , Shyamsundar Rajaram , Robert Oliver Burns Zeldin
IPC: G06F15/173 , H04L12/24 , H04L12/26 , H04L29/08 , G06Q30/02
Abstract: The present disclosure is directed toward systems, methods, and non-transitory computer readable media that use multi-point optimization for delivery of digital content by way of a digital content distribution platform. In particular, one or more embodiments described herein receive a content item from a content provider to be displayed to users of the platform. The embodiments optimize delivery to obtain a first target event and determine metrics that delivery of the content item is expected to satisfy. If the actual metrics of delivery fail to satisfy the expected metrics, delivery is re-optimized to obtain either the first target event or a second target event.
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公开(公告)号：US20190102784A1
公开(公告)日：2019-04-04
申请号：US15722126
申请日：2017-10-02
Applicant: Facebook, Inc.
Inventor： Zheng Chen , Robert Oliver Burns Zeldin , Shyamsundar Rajaram , Hao Song , Nimish Rameshbhai Shah
IPC: G06Q30/02
Abstract: A bidding system determines values for impression opportunities on an online system. Values are determined by a set of models. Each model of the set of models is associated with a user response and predicts the likelihood that the associated user response will occur following an impression. The models are ordered based on a predicted chronological ordering of user actions that lead towards a conversion. Each model is weighted based on its relevance to conversion and the accuracy of the model relative to the other models in the set of models. Predictions of the probability of user action generated by each model, as well as the model weights, are used to determine a value for impression opportunities. Data from impression opportunities are then used to further train the models and update the weights assigned to each model for use in determining values for subsequent impression opportunities.
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