Microsoft Technology Licensing, LLC

Yahoo! Inc.

Linkedin Corporation

At&t Intellectual Property I, L.p.

Excalibur Ip, LLC

Deepak Agarwal

Bee-Chung Chen

Liang Zhang

Yang Yang

Wei Chu

Raghu Ramakrishnan

Pradheep K Elango

Shaunak Chatterjee

Bo Long

Shakti Dhirendraji Sinha

Frank Emmanuel Astier

Dhruv Arya

David Q He

Sanjay Sureshchandra Dubey

Viet Thuc Ha

Nitin Motgi

Ruixuan Hou

Seung-Taek Park

Ritesh Maheshwari

Steven Scott Noble

Jonathan David Traupman

Jieying Chen

Jia Li

Andrei Z Broder

Vanja Josifovski

Todd Beaupre

Ashvin Kannan

Nihar Niranjan Mehta

Scott Roy

H Scott Roy

Lihong Pei

Toon Sripatanaskul

Guangyu Dong

Raghunath Ramakrishnan

Kai Wei

Bee-Chung Cheng

Shanmugasundaram Ravikumar

Krishnaram Kenthapadi

Malcolm G Slaney

Robert Hall

Marcus Felipe Fontoura

Qi He

Stuart MacDonald Ambler

Ahmad A Abdulkader

Deepayan Chakrabarti

John Thrall

Mohammad Mahdian

Zheng Li

Long-ji Lin

John Anthony Tomlin

Srujana Merugu

Evgeniy Gabrilovich

Ajith Muralidharan

Michael David Revow

Mohamed Gamal Mohamed Mahmoud

Jian Yang

Amitabh Seth

Colin Goodall

Aparna Lakshmiratan

Howard Scott Roy

Ravi Kiran Holur Vijay

Vik Singh

Yijie Wang

Romer E Rosales

Peiji Chen

Yitong Zhou

R Preston McAfee

Vijay Krishna Narayanan

Alnur Ali

Dz-Mou Jung

Yiming Ma

Mark D Sordo

David Reiley

Mikhail Obukhov

Guangde Chen

Sai-Ming Li

Sameer Yusufali Merchant

Seyed Ali Ahmadi

William H DuMouchel

Joaquin Arturo Delgado Rodriguez

Victor K Chu

Donald Swanson

Danny Zhang

Joe Zachariah

Sharat Israni

Chandar Venkataraman

Amit Phadke

Michael Salisbury

Kwei-you Tao

Dejan Diklic

XianXing Zhang

Viral Gupta

In an example embodiment, a machine learned model is used to determine whether to send a notification for a feed object to a user. This machine learned model is optimized not just based on the likelihood that the notification will cause the user to interact with the feed object, but also the likely short-term and long-term impacts of the user interacting with the feed object. This machine learned model factors in not only the viewer's probability of immediate action, such as clicking on a feed object, but also the probability of long-term impact, such as the display causing the viewer to contribute content to the network or the viewer's response encouraging more people to contribute content to the network. As such, the machine learned model is optimized not just on notification interactivity but also on feed objects interactivity.

Joint optimization of notification and feed

Techniques for predicting a user response to content are described. According to various embodiments, a configuration file is accessed, where the configuration file includes a user-specification of raw data accessible via external data sources and raw data encoding rules. In some embodiments, the raw data includes raw member data associated with a particular member and raw content data associated with a particular content item. Thereafter, source modules encode the raw data from the external data sources into feature vectors, based on the raw data encoding rules. An assembler module assembles one or more of the feature vectors into an assembled feature vector, based on user-specified assembly rules included in the configuration file. A prediction module performs a prediction modeling process based on the assembled feature vector and a prediction model, to predict a likelihood of the particular member performing a particular user action on the particular content item.

Systems and methods for content response prediction

In an example embodiment, usage information is used to calculate one or more individual document historical information-deficient metrics (IDHIDMs) by combining values for the one or more metrics from multiple documents within the document corpus that share an identical combination of entities of the one or more entity types. A search query is segmented into a plurality of segments, wherein at least one of the plurality of segments is tagged as a first entity type and at least one of the plurality of segments is tagged as a second entity type. At least one for a combination of entities matching the tagged segments is used to rank one or more retrieved documents responsive to the query.

Entity-faceted historical click-through-rate

A system and method for intermediate landing page rerouting are provided. In example embodiments, determine whether a webpage associated with a hyperlink has corresponding social network activities. Extract content from the webpage determined to have corresponding social network activities. In response to a selection of the hyperlink, reroute a web browser to an intermediate landing page. Cause presentation, at a user interface, of the extracted content and the corresponding social network activities.

Rerouting to an intermediate landing page

A system and method may optional include or utilize a processor configured to receive a request for social network content for display in a sponsored content position in a newsfeed of a social network interface, the position having a position criterion, identify a sponsored content item of multiple sponsored content items stored on a database based, at least in part, on a characteristic of the sponsored content item meeting the position criterion, a bid associated with the sponsored content item, and a scaling factor, wherein each of the sponsored content items correspond to one of multiple item types and at least two of the sponsored content items are of a different item type. The scaling factor for each of the sponsored content items is based on the item type of the corresponding one of the sponsored content items.

System and method for positioning sponsored content in a social network interface

A system and method of adjusting an affinity score between an entity pair in a social network is disclosed. The method may include determining, with a processor, whether a first member of the entity pair is a heavy user member. The method further includes if the first member is the heavy user member, determining, with the processor, an affinity adjustment factor between the first member and the second member, and adjusting, with the processor, the affinity score between the first member and the second member of the entity pair in accordance with the adjustment factor to determine an adjusted affinity score. The method may include determining, with the processor, whether a number of interactions on content items indicates that the first member is the heavy user member. The second member is associated with a content item that is being considered for display to the first member.

Adjusting heavy users' affinity for heavy user entity-pairs in a social network

In an example, a set of cohort types and an anonymized set of confidential data data values for a plurality of cohorts having cohort types in the set of cohort types are obtained. Then it is determined, from a set of candidate data transformations, a best fitting data transformation for the anonmyized set of confidential data data values. The anonymized set of confidential data data values is transformed using the best fitting data transformation. Optimal smoothing parameters are computed for each cohort type. Then, for each cohort in the set of cohort types having a small sample size, a best parent for the cohort is determined and a posterior distribution for the cohort is determined based on the best parent for the cohort and the optimal smoothing parameters for a cohort type for the cohort.

Computing smoothed posterior distribution of confidential data

A system for processing data is provided. During operation, the system obtains a current window of one or more intervals of timeseries data collected from a monitored system. Next, the system continuously performs a statistical hypothesis test that compares the one or more intervals of the time-series data with baseline values from historic time-series data associated with the monitored system. When the statistical hypothesis test indicates a deviation of the time-series data from the baseline values, the system outputs an alert of an anomaly represented by the deviation.

Statistical detection of site speed performance anomalies

A system and method optionally includes or utilizes a processor may receive a request for social network content for display in a position of a plurality of sponsored content positions in a newsfeed of a social network interface, each of the plurality of sponsored content positions having a position criterion, at least two of the plurality of sponsored content positions having a different position criterion and identify a sponsored content item of a plurality of sponsored content items stored on a database based, at least in part, on a characteristic of the sponsored content item meeting the position criterion and a bid associated with the sponsored content item. A transmitter may transmit the sponsored content item from the processor to a server for display on a user interface.

In an example, a plurality of member profiles in a social networking service are obtained, each member profile identifying a member and listing one or more skills the corresponding member has explicitly added to the member profile, the one or more skills indicating a proficiency by the member in the corresponding skill. A members-skills matrix is formed, wherein each cell in the matrix is assigned a value based on whether the corresponding member has the corresponding skill. The dot product of the members matrix and the skills matrix is then computed and used to identify one or more latent skills of a first member of the social networking service. Then a first digitally stored member profile is augmented with the one or more latent skills by combining the one or more latent skills with explicit skills for purposes of one or more searches that utilize member skills as an input variable.

Entity-aware features for personalized job search ranking

This disclosure relates to systems and methods that include a member activity database including data indicative of interactions with content items on a social network by a population of users of the social network. A processor is configured to obtain an optimization criterion based on at least two constraints related to a performance of the social network, obtain, for a subset of the population of users, at least some of the data indicative of interactions with content items from the member activity database, determine, based on the at least some of the data as obtained, an operating condition for the social network that is estimated to meet the optimization criterion, and provide, to at least some of the user devices via the network interface, the social network based, at least in part, on the operating condition.

Subset multi-objective optimization in a social network

The disclosed embodiments provide a system for processing data. During operation, the system obtains a set of components of a time-series performance metric associated with an anomaly in a performance of one or more monitored systems. For each component in the set of components, the system performs a statistical hypothesis test on the component to assess a deviation of the component from a baseline value of the component. When the statistical hypothesis test identifies a statistically significant deviation of the component from the baseline value, the system outputs an alert comprising a root cause of the anomaly that is represented by the statistically significant deviation of the component from the baseline value.

Root cause investigation of site speed performance anomalies

The disclosed embodiments provide a system for processing data. During operation, the system obtains a component of a time-series performance metric associated with a server-side root cause of an anomaly in the time-series performance metric. Next, the system obtains a call graph representation of the component, wherein the call graph representation includes a parent node having a parent value of the component and a set of child nodes of the parent node, each child node having a corresponding child value of the component. The system then analyzes the call graph representation to identify one or more of the child nodes as sources of the anomaly. Finally, the system outputs an alert that identifies the sources of the anomaly.

Analysis of site speed performance anomalies caused by server-side issues

A social networking system receives from a member an item for sharing on the social networking system. The system determines whether the item for sharing is a first sharing for the member or whether the member has not shared an item for a time period that transgresses a threshold. When the item for sharing is a first sharing or a sharing that transgresses the threshold, the system marks the item for a promotion in a feed of another member of the social networking system.

Social incentive optimization in online social networks

The disclosed embodiments provide a system for processing data. During operation, the system obtains a global version of a statistical model and a user-specific version of the statistical model for a user. Next, the system applies the global version to member features of the user and job features of a set of jobs to generate a first ranking of the jobs for the user. The system then applies the user-specific version to the member features and the job features for a highest-ranked subset of jobs in the first ranking to generate a second ranking of the jobs for the user. Finally, the system outputs at least a portion of the second ranking as a set of job recommendations.

Scalable response prediction using personalized recommendation models

Growing community of inventors

Sunnyvale, CA, United States of America

Deepak Agarwal