1 research outputs found
Heterogeneous Edge Embeddings for Friend Recommendation
We propose a friend recommendation system (an application of link prediction)
using edge embeddings on social networks. Most real-world social networks are
multi-graphs, where different kinds of relationships (e.g. chat, friendship)
are possible between a pair of users. Existing network embedding techniques do
not leverage signals from different edge types and thus perform inadequately on
link prediction in such networks. We propose a method to mine network
representation that effectively exploits heterogeneity in multi-graphs. We
evaluate our model on a real-world, active social network where this system is
deployed for friend recommendation for millions of users. Our method
outperforms various state-of-the-art baselines on Hike's social network in
terms of accuracy as well as user satisfaction.Comment: To appear in ECIR, 201