A new article on Social Media Today reports that, as part of its broader push to help maximize economic opportunity and remove inherent bias from its systems, LinkedIn has refined the recommendation algorithm for its ‘People You May Know’ feature. The aim is to ensure that users are not adversely impacted by various factors – such as where they grew up, where they went to school, or where they work – when building their professional networks.
LinkedIn’s ‘People You May Know’ recommendations can play a key role in guiding user connections. For that reason, LinkedIn has sought to improve its systems to stop certain user types from dominating this element.
“LinkedIn’s mission is to be a place that is focused on creating economic opportunity and driving more equitable outcomes for every member of the global workforce. A key part of that mission is enabling people to connect with each other and build online professional networks. Our latest work to optimize member experiences and create more equity in connection opportunities ties to one of our foundational features of network building on LinkedIn, our People You May Know (PYMK) recommendation system.
PYMK has been a long-standing part of the LinkedIn platform and is powered by some of our earliest machine learning (ML) algorithms. The goal of PYMK is to help members connect to people who may be relevant additions to their professional networks. PYMK primarily uses data like the Economic Graph and platform interactions to mine features and use ML algorithms to come up with relevant recommendations. Specifically, it uses a combination of linear and non-linear models to estimate the propensity to connect between two members. This probability generates a P(connect) score, and PYMK subsequently recommends a list of potential new connections using members ranked according to this score.
Over the last year, we made several changes to the underlying PYMK algorithms in order to improve the PYMK experience for all members. These changes had the practical effect of making PYMK a more equitable feature by making it more effective for members regardless of their existing network strength or frequency of platform usage–rather than disproportionately serving “power users” of the site.”
LinkedIn’s improved system now seeks to better balance recommendations to highlight people who are not seeing as many connections requests. The result is that a wider pool of people are now receiving more connection requests, as opposed to the majority going to fewer members – which provides more opportunities for users to grow their networks on the platform.