The Hype in Professional Social Matching: A Closer Look to Applications and Research
As a part of our current Big Match research project, we’ve reviewed what has happened in the field of professional social matching in the last couple of years: interesting startups, new apps, relevant news articles, research papers, etc. Here’s a sneak preview of the findings!
First, the marketspace for idea of “Tinder for jobs” seems pretty crowded already. In recent years, there has been a rise in startups that are developing applications where jobseekers are somehow matched with jobs. PIIK, Tiitus, Pockethunt and Treamer are some examples of the Finland-based startups. Also, Estonia-based MeetFrank has recently gained recognition and has been, for example, the official recruiting application at the Slush event. Last year, the Finnish government stated that it wants to make Finland a leader in the application of artificial intelligence. With the digitalization push, the beta version of Työmarkkinatori is going to be available in February.
Recently, there was also Sitra-funded Ratkaisu 100 competition with one-million-euro prize pool where many solutions were based on the idea of matching. The finalists included “Tinder for mentoring” and Headai, which uses artificial intelligence to chart human expertise using open data on the internet. Headai was eventually one of the winners — congratulations!
Worldwide, it seems that there are also matching applications for matching like-minded people. Some examples are Glynk, Opin, WhatTuDu, and citysocializer. Some of them, like Australian-based Affinity, even lists research papers on its website about how similar people attract each other. Interestingly, we were not able to find apps that match people who are unlike or have complementary skills or viewpoints.
In addition to “Tinder for jobs” and “matching like-minded people” applications, there are already many startups and applications around making matches in events and networking in general. In Finland, maybe the most well-known apps are currently Brella and Mingla. Worldwide, some of the best know apps are b2match, Grip, Converve, Swapcard and Mixtroz. Events offer great opportunity to find new people and in research, there have been some studies, for example, on finding collaborators in academic conferences.
The problem with small-scale applications by startups is to have enough users to create trustworthy recommendations or matches. It is easier to imagine more success to applications that are built on top of already successful social media platforms. Last year alone, Google introduced Google for Jobs “to bring you the most comprehensive listing of jobs”, Facebook introduced “Discover People” feature “to help you make more friends” and LinkedIn rolled out “Career Advice” feature. These new algorithms have not come without problems. For example, Gizmodo wrote how Facebook’s People You May Know feature can make too good recommendations, and in Finland, some have speculated how AI in recruiting can become biased and even racist. This “dark side” of matchmaking is interesting also from the perspective of research.
In research, Loren Terveen and David W. McDonald outlined a framework and research agenda for social matching in a research paper published already in 2005. After that, especially the research from Julia Mayer, Li Chen, Marko Tkalcic and Peter Brusilovsky have been central to the research on social matching. Researchers have found that matching in current recommender systems are usually based on some similarity mechanism and there is growing interest to make recommendations more serendipitous and diverse. The topic of social matching calls for more multidisciplinary research and user testing with concrete prototypes.
