Groups You May Like (GYML), is a personalized group recommendation feature powered by LinkedIn’s Recommendation Engine.
“With GYML, not only can members easily find people with related interests, but they can also engage with communities on specific topics related to their professional expertise. Once you’ve found a group of your liking, users have always asked for similar groups that they can check out or participate in. Rather than going through the same laborious process of finding that suitable group, GYML lets you find them instantaneously,” informs Alexis Pribula.
Here’s a simple explanation on the inner workings of “Groups You May Like” on LinkedIn:
- “Metric: We designed the metric to optimize for participation in the community and not necessarily only for group affinity. Indeed, what makes a group valuable, along with its members, is the member contributions to the professional dialogue. This design can be achieved by an approach we at LinkedIn call ‘data jiu jitsu’: First, match a group to a member based on content affinity, then optimize for the desired behavior (in our case “participation”) which can be done via social learning. Social learning theory tells us that individuals learn by observing other’s behavior and the outcomes of those behaviors. Hence, someone joining a group with high participation from its members is more likely to engage further in the future.
- Key features: One of the most interesting aspects of GYML are the group features definitions. Beyond the usual suspects that include group title and group description, the real DNA of a group resides within its members. Hence, using a construct of information theory called Mutual Information we generate a “virtual” group profile which, following the homophily concept, can be matched against each member. Another source of information we use as a feature for matching is the popularity of the group in someone’s network. If many of your connections belong to a group, that group will probably be of interest to you.
Two interesting edge cases arose with this initial approach: potential mismatch with alumni groups (spurring strong reactions from members) and location specific groups, like “Yahoo India” for e.g. This was resolved by implementing filters that discard groups with an over-representation of a school (location) that does not match the member’s school (location).
- Historical Data: To fine-tune the matching process, we leveraged historical data focusing on recent group joins on LinkedIn. To keep the best possible relevance in our matching algorithm, we also applied some filtering. First, we filtered out groups which our members may find controversial. Second, we did not show group recommendations to spammers: members who try to join groups for the only purpose of spamming the group were subsequently removed from the groups,” explained Pribula.