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公开(公告)号：US20200302333A1
公开(公告)日：2020-09-24
申请号：US16359914
申请日：2019-03-20
Applicant: Microsoft Technology Licensing, LLC
Inventor： Jinyun Yan , Vinay Praneeth Boda , Yin Zhang , David Pardoe
IPC: G06N20/00 , G06N5/04 , G06F3/0482
Abstract: Techniques for controlling item frequency using machine learning are provides. In one technique, two prediction models are trained: one based on interaction history of multiple content items by multiple entities and the other based on predicted interaction rates and an impression count for each of multiple content items. In response to a request, a particular entity associated with the request is identified and multiple candidate content items are identified. For each identified candidate content item, the first prediction model is used to determine a predicted interaction rate, an impression count of the candidate content item is determined with respect to the particular entity, the second prediction model is used to generate an adjustment based on the impression count, and an adjusted entity interaction rate is generated based on the predicted interaction rate and the adjustment. A particular candidate content item is selected based on the generated adjusted entity interaction rates.
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公开(公告)号：US20200210908A1
公开(公告)日：2020-07-02
申请号：US16232862
申请日：2018-12-26
Applicant: Microsoft Technology Licensing, LLC
Inventor： Keqing Liang , Wen Pu , Sahin C. Geyik , Yu Wang , Ying Chen , Yin Zhang , Sumedha K. Swamy
Abstract: The disclosed embodiments provide a system for performing dynamic job bidding optimization. During operation, the system obtains historical data containing a time series of interactions with a job. Next, the system uses the historical data to calculate an initial price of a job based on a predicted number of interactions with the job. The system then determines a first dynamic adjustment to the initial price that improves utilization of a budget for the job and a second dynamic adjustment to the initial price that improves a performance of the job. Finally, the system applies the first and second adjustments to the initial price to produce an updated price for the job and delivers the job within an online system based on the updated price.
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Patent Agency Ranking
公开(公告)号：US11055751B2
公开(公告)日：2021-07-06
申请号：US15610529
申请日：2017-05-31
Applicant: Microsoft Technology Licensing, LLC
Inventor： Jan Schellenberger , Yang Zhao , Yin Zhang , David Pardoe
IPC: G06Q30/02 , H04L12/911 , H04L12/825
Abstract: Techniques for controlling resource usage in a computing environment are provided. In one technique, a target resource usage for a particular point in time is determined for a content delivery campaign. Determining, for the content delivery campaign, a current resource usage for the particular point in time. Also, a bandwidth associated with the target resource usage at the particular point in time is determined. Based on a difference between the current resource usage and one or more boundaries of the bandwidth, a throttling factor is calculated. Based on the throttling factor, a probability of the content delivery campaign participating in a content item selection event is determined.
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公开(公告)号：US20210103861A1
公开(公告)日：2021-04-08
申请号：US17126546
申请日：2020-12-18
Applicant: Microsoft Technology Licensing, LLC
Inventor： Keqing Liang , Wen Pu , Sahin Cem Geyik , Yu Wang , Ying Chen , Yin Zhang , Sumedha K. Swamy
Abstract: The disclosed embodiments provide a system for performing dynamic job bidding optimization. During operation, the system obtains historical data containing a time series of interactions with a job. Next, the system uses the historical data to calculate an initial price of a job based on a predicted number of interactions with the job. The system then determines a first dynamic adjustment to the initial price that improves utilization of a budget for the job and a second dynamic adjustment to the initial price that improves a performance of the job. Finally, the system applies the first and second adjustments to the initial price to produce an updated price for the job and delivers the job within an online system based on the updated price.
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公开(公告)号：US11093861B2
公开(公告)日：2021-08-17
申请号：US16359914
申请日：2019-03-20
Applicant: Microsoft Technology Licensing, LLC
Inventor： Jinyun Yan , Vinay Praneeth Boda , Yin Zhang , David Pardoe
IPC: G06N20/00 , G06F3/0482 , G06N5/04
Abstract: Techniques for controlling item frequency using machine learning are provides. In one technique, two prediction models are trained: one based on interaction history of multiple content items by multiple entities and the other based on predicted interaction rates and an impression count for each of multiple content items. In response to a request, a particular entity associated with the request is identified and multiple candidate content items are identified. For each identified candidate content item, the first prediction model is used to determine a predicted interaction rate, an impression count of the candidate content item is determined with respect to the particular entity, the second prediction model is used to generate an adjustment based on the impression count, and an adjusted entity interaction rate is generated based on the predicted interaction rate and the adjustment. A particular candidate content item is selected based on the generated adjusted entity interaction rates.
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公开(公告)号：US20190297028A1
公开(公告)日：2019-09-26
申请号：US15927979
申请日：2018-03-21
Applicant: Microsoft Technology Licensing, LLC
IPC: H04L12/911
Abstract: Techniques are provided for controlling resource usage in a computing environment. In response to receiving a content request, a set of candidate content delivery campaigns is identified. For a first candidate content delivery campaign in the set, an anticipated resource usage of a resource that is associated with the first candidate content delivery campaign is determined. The anticipated resource usage is determined based on (1) a resource reduction per event of each event in a set of detected events of a content item of the first candidate content delivery campaign and (2) a decay factor. Based on the anticipated resource usage, it is determined whether the first candidate content delivery campaign is to be removed from the set.
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公开(公告)号：US20180349964A1
公开(公告)日：2018-12-06
申请号：US15610529
申请日：2017-05-31
Applicant: Microsoft Technology Licensing, LLC
Inventor： Jan Schellenberger , Yang Zhao , Yin Zhang , David Pardoe
IPC: G06Q30/02 , H04L12/825 , H04L12/927 , H04L12/911
Abstract: Techniques for controlling resource usage in a computing environment are provided. In one technique, a target resource usage for a particular point in time is determined for a content delivery campaign. Determining, for the content delivery campaign, a current resource usage for the particular point in time. Also, a bandwidth associated with the target resource usage at the particular point in time is determined. Based on a difference between the current resource usage and one or more boundaries of the bandwidth, a throttling factor is calculated. Based on the throttling factor, a probability of the content delivery campaign participating in a content item selection event is determined.
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