Heterogeneous Edge Embeddings for Friend Recommendation

Janu Verma; Srishti Gupta; Debdoot Mukherjee; Tanmoy Chakraborty

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arXiv · 1902.03124

Heterogeneous Edge Embeddings for Friend Recommendation

Janu Verma·Srishti Gupta·Debdoot Mukherjee·Tanmoy Chakraborty

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Abstract

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.

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Janu Verma, Srishti Gupta, Debdoot Mukherjee, Tanmoy Chakraborty. 2019-02-07. Heterogeneous Edge Embeddings for Friend Recommendation. https://arxiv.org/abs/1902.03124

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