Microsoft Technology Licensing, LLC

Linkedin Corporation

Google Inc.

Xin Fu

Alexis Blevins Baird

Yang Zhou

Jason Schissel

Joonhyung Lim

Sathyanarayan Anand

Guangde Chen

Huangming Xie

Yu Long Cao

Nikita Igorevych Lytkin

Ying Chen

Lokesh Pooranmal Bajaj

Brad Christopher Ciraulo

Yin Lou

Damien Albert Louis Coullon

Raviraj Jain

Zhoutong Fu

Bhavana Chowdary Challa

Da Huo

Barry P Huang

Seyedehmina Doroud

Lixin Feng

The disclosed embodiments provide a system for processing data. During operation, the system obtains a count of actions associated with a member of a social network, wherein the count comprises a set of action types and a number of actions associated with the member for each of the action types. Next, the system uses the count to calculate a set of scores for measuring the value of the social network to the member, wherein the set of scores is associated with researching and contacting people, building a network, keeping up with connections, staying informed and building knowledge, establishing and managing a reputation, and getting hired. The system then outputs the scores for use in characterizing and improving the value of the social network for the member.

Measuring member value in social networks

Techniques for dynamically generating feedback based on contextual information and providing the feedback to a user are provided. A service provider determines, based on contextual information related to a user (such as personal information previously provided by the user) interactive data to display to the user. The type of interactive data may correspond to the type of personal information submitted by the user or derived by the service provider. The interactive data may be based on other users that have already registered with the service provider. In this way, a user is able to (1) view current and relevant information that is related to the user and (2) see the value that the user receives by providing additional personal information. Subsequent interactive data may be based on multiple personal data items that the service provider receives from the user. These techniques are also applicable after a user has registered with a service provider.

Dynamically generating feedback based on contextual information

System and techniques for activity sensing online preference assay are described herein. An initial indication of an online activity preference for a member of a social network service may be obtained. A plurality of member activities corresponding with the online activity preference for a period of time subsequent to obtaining the initial indication may be collected. Respective decision trees of a set of decision trees may be traversed based on a set of inputs comprising the collected plurality of member activities to determine a probability that the online activity preference corresponds with the member. An actual online activity preference may be derived for the member using an aggregation of the determined probability for the respective decision trees of the set of decision trees. Social network content items may be filtered for the member based on the actual online activity preference.

Activity sensing online preference assay

In order to invite individuals to join a social network, telephone contact information of a user of the social network is extracted, with the user's permission, from a phonebook associated with a telephone application on the user's portable electronic device. Then, the extracted telephone contact information is shared across different platforms to the user's desktop computer, and telephone numbers in the extracted telephone contact information are identified. These telephone numbers are associated with other portable electronic devices, and with individuals that are not currently members of the social network. After receiving confirmation of a telephone number from the user (e.g., via the desktop computer), the location of the associated portable electronic device is determined. Based on the telephone number and the location, an invitation to join the social network is conditionally provided to the portable electronic device, via a Short Message Service (SMS) message.

Providing invitations based on cross-platform information

Techniques for tracking profile completeness among members of an online social networking system are described. According to various embodiments, profile completion score criteria information identifies a profile completion score weight value associated with each of a plurality of member profile fields available in member profiles of an online social networking service. A specific member profile associated with a specific member of the online social networking service is accessed, and one or more of the plurality of member profile fields that have been completed in the specific member profile are identified. Thereafter, the profile completion score weight values associated with the member profile fields that have been completed in the specific member profile are determined, based on the profile completion score criteria information. Based on the determined profile completion score weight values, a profile completion score is then generated for the specific member profile.

Profile completion score

Techniques for optimizing a guided edit process for editing a member profile page are described. According to various embodiments, incomplete member profile fields in a member profile associated with member of an online social networking service are identified. Profile completion score weight values associated with the incomplete member profile fields in the member profile are then determined. Thereafter, the incomplete member profile fields in the member profile are ranked, based on the profile completion score weight values associated with each of the incomplete member profile fields. A list of one or more of the highest ranked incomplete member profile fields are then displayed, together with a prompt recommending the member to complete the identified incomplete member profile fields.

Guided edit optimization

The disclosed embodiments relate to a system for analyzing performance in an online professional network. During operation, the system receives time series data for user actions, wherein for each user action, the time series data comprises a series of numbers associated with consecutive time intervals, wherein a given number indicates a number of times the user action occurred during the time interval. The system also receives time series data for performance metrics, wherein for each performance metric, the time series data comprises a series of numbers associated with consecutive time intervals, wherein a given number indicates the number of times the performance metric occurred during the time interval. The system then performs a time series analysis on the received time series data for user actions and performance metrics to determine relationships between the user actions and the performance metrics.

Time series technique for analyzing performance in an online professional network

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for identifying listings of multi-site entities based on user behavior signals. In one aspect, a method exploits user behavioral signals that are strong indicators of whether a search result previously presented for a multi-site-entity search query is, in fact, a relevant result, and affords sufficient weight to these behavioral signals to overcome other signals of relevance for the search result. Two such strong user behavior signals include (i) users invoking an information window from a search result and (ii) users submitting a direction request related to a search result. A black list of search results and/or a search result index can be generated based on how the two strong behavior signals associated with the search results compare to threshold values set for the two strong behavior signals.

Identifying listings of multi-site entities based on user behavior signals

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Santa Clara, CA, United States of America

Xin Fu