Heterogeneous Edge Embeddings for Friend Recommendation
This addresses friend recommendation for users in social networks with multiple relationship types, but it is incremental as it builds on existing network embedding techniques.
The paper tackled link prediction for friend recommendation in multi-graph social networks by proposing a method that leverages edge type heterogeneity, resulting in improved accuracy and user satisfaction on a real-world network with millions of users.
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.