Abstract: There are a subset of members at LinkedIn who are receiving a large amount of irrelevant connection requests. Once they have accepted these requests, their network becomes overrun with irrelevant feed updates and notifications. At LinkedIn, we have seen this both in the data and feedback from our members. We have looked deeply into healthy networks and how we can help our members achieve them.
One approach is being smarter at recommending potential connections. When AI models are built to recommend member connections, the primary objective is to maximize the probability of forming a connection. However, this leads to some members receiving too many invitations. We addressed this issue by accounting for the recipient's invitation volume through a decay model, which reduces the appearance of members who have received too many invitations from other members’ recommended list.
Evaluating the impact of the decay model is a challenging problem with both sender and receiver side impact as well as a long term network effects. Sender impact can be measured directly through AB testing. Recipient side impact can be measured through an attribution framework. For long term network effects, we’ve designed special experiments to understand this more.
The learning objectives are:
Consider the experience of both sender and receiver in modeling the social network
Understand how the density of connection requests impacts the LinkedIn experience
Measure the impact of recommendations from both sender and receiver sides holistically
Bio: Qiannan is a Senior Data Scientist at LinkedIn focusing on the growth of the Flagship product. She has led multiple data science projects, concentrating on the quality growth of social networks, the measurement of the network effect in experiments, and member record linkage. She is an expert in providing data-driven insights and product recommendations. She holds a doctorate in statistics from the Department of Statistics at the University of Virginia where her research topic was on detecting clusters in the neural network of a human brain.