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LinkedIn /ˌlɪŋkt.ˈɪn/ is a business-oriented social networking service. Founded in December 2002 and launched on May 5, 2003, it is mainly used for professional networking. In 2006, LinkedIn increased to 20 million members. As of June 2013, LinkedIn reports more than 259 million acquired users in more than 200 countries and territories.The site is available in 20 languages, including Chinese, English, French, German, Italian, Portuguese, Spanish, Dutch, Swedish, Danish, Romanian, Russian, Turkish, Japanese, Czech, Polish, Korean, Indonesian, Malay, and Tagalog. As of 2 July 2013, Quantcast reports LinkedIn has 65.6 million monthly unique U.S. visitors and 178.4 million globally, a number that as of 29 October 2013 has increased to 184 million. In June 2011, LinkedIn had 33.9 million unique visitors, up 63 percent from a year earlier and surpassing MySpace. LinkedIn filed for an initial public offering in January 2011 and traded its first shares on May 19, 2011, under the NYSE symbol "LNKD". Wikipedia.

Meng X.,LinkedIn | Mahoney M.W.,Stanford University
Proceedings of the Annual ACM Symposium on Theory of Computing | Year: 2013

Low-distortion embeddings are critical building blocks for developing random sampling and random projection algorithms for common linear algebra problems. We show that, given a matrix A ∈Rn×d with n ≫ d and a p ∈ [1; 2), with a constant probability, we can construct a low-distortion embedding matrix ∏ ∈ RO(poly(d))×n that embeds Ap, the ℓp subspace spanned by A's columns, into (RO(poly(d)); ∥ · ∥p); the distortion of our embeddings is only O(poly(d)), and we can compute ∏A in O(nnz(A)) time, i.e., input-sparsity time. Our result generalizes the input-sparsity time ℓ2 subspace embedding by Clarkson and Woodruff [STOC'13]; and for completeness, we present a simpler and improved analysis of their construction for ℓ2. These input-sparsity time ℓp embeddings are optimal, up to constants, in terms of their running time; and the improved running time propagates to applications such as (1 ± ε)-distortion ℓp subspace embedding and relative-error ℓp regression. For ℓ2, we show that a (1 + ε)-approximate solution to the ℓ2 regression problem specified by the matrix A and a vector b ∈ Rn can be computed in O(nnz(A) + d3 log(d=ε)= ε2) time; and for ℓp, via a subspace-preserving sampling procedure, we show that a (1 ± ε)-distortion embedding of Ap into RO(poly(d))can be computed in O(nnz(A) · log n) time, and we also show that a (1 + ε)-approximate solution to the ℓp regression problem minx∈Rd ∥Ax-b∥ p can be computed in O(nnz(A) · log n + poly(d) log(1/ε)/ε2) time. Moreover, we can also improve the embedding dimension or equivalently the sample size to O(d3+p/2 log(1/ε)/ε2) without increasing the complexity. Copyright 2013 ACM. Source

LinkedIn | Date: 2015-10-28

A fact checking system is able to verify the correctness of information and/or characterize information by comparing the information with one or more sources. The fact checking system automatically monitors, processes, fact checks information and indicates a status of the information. Fact checking results are able to be validated by re-fact checking the fact check results.

LinkedIn | Date: 2015-10-27

An Internet-based system capable of automatically maintaining contact-related information in any computer software application or digital device which stores or manages contact-related information. More particularly, the system allows users to automatically enter and maintain contact-related information in a digital address book or similar application or device (such as a wireless phone or PDA) with minimal or no manual entry of the contact-related information by the user. The system also allows contacts of the user (i.e. people whos contact information or partial contact information is present in the users address book application) to make corrections to their contact-related information contained in the users address book, request reciprocal contact information from the user, deny the user access to additional or corrected contact-related information and take other actions relative to managing the contact-related information which others (users) have about them.

LinkedIn | Date: 2015-01-13

According to various exemplary embodiments, it is determined that a particular user is associated with a particular member segment of a networking website. Further, a task prioritization list associated with the particular member segment is accessed. The task prioritization list may include a prioritized list of profile update tasks associated with successful user profile pages of the particular member segment. Moreover, a prompt is displayed inviting the particular user to update the particular user profile page based on the task prioritization list (e.g., by referring to the member segment and at least one of the tasks in the task prioritization list).

A method and system for evaluating the reputation of a member of a social networking system is disclosed. Consistent with an embodiment of the invention, one or more attributes associated with a social networking profile of a member of a social network are analyzed. Based on the analysis, a ranking, rating or score is assigned to a particular category of reputation. When requested, the ranking, rating or score is displayed to a user of the social network.

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