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 te...

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Bibliographic Details
Published in:arXiv.org 2019-02
Main Authors: Verma, Janu, Gupta, Srishti, Mukherjee, Debdoot, Chakraborty, Tanmoy
Format: Article
Language:English
Subjects:
Embedded systems
Graphical representations
Graphs
Recommender systems
Social networks
State of the art
User satisfaction
Online Access:Get full text
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Summary: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.
ISSN:2331-8422