Abstract
We propose a friend recommendation system (an application of link prediction) using edge embedding 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 edge 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 metrics as well as user satisfaction.
J. Verma and S. Gupta—equal contribution.
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Verma, J., Gupta, S., Mukherjee, D., Chakraborty, T. (2019). Heterogeneous Edge Embedding for Friend Recommendation. In: Azzopardi, L., Stein, B., Fuhr, N., Mayr, P., Hauff, C., Hiemstra, D. (eds) Advances in Information Retrieval. ECIR 2019. Lecture Notes in Computer Science(), vol 11438. Springer, Cham. https://doi.org/10.1007/978-3-030-15719-7_22
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