Abstract
Mention recommendation is the task of recommending the right candidate users in a message. Many works have been conducted on the problem of whom to mention. However, due to the sparsity and heterogeneous of mention data, none of them well solve the problem. The recent advances in network embedding representation learning provide an effective approach to model the sparsity and heterogeneous simultaneously in heterogeneous information network. To this end, we propose a novel Network Embedding Mention (NEM) recommendation model to recommend the right users in a message. NEM constructs a heterogeneous mention network based on different relationships among different entities. Then NEM learns a unified low dimensional embedding vector using random walk for users and messages by considering network structure and vertex content information. Finally, whom to mention is ranked by calculating the relevance scores from heterogeneous user and message embeddings. To evaluate the proposed method, we construct a large dataset and their corresponding social networks from a real-world social media platform. Through extensive experiments on real-world mention collection, we demonstrate that our proposed model outperforms the previous state-of-the-art methods in term of recommendation task.
Highlights
With the exponential growth of of social networks such as Twitter and Weibo, a lot of information is created and spread by millions of users every day on these platforms
Motivated by the network embedding, we propose a novel Network Embedding Mention recommendation model, called NEM, to recommend the right users in a message
We propose a novel mention recommendation model based on deep random walk algorithm on the heterogeneous mention network
Summary
With the exponential growth of of social networks such as Twitter and Weibo, a lot of information is created and spread by millions of users every day on these platforms. Information flow among users is very easy to form large-scale cascade diffusion through social connections. This feature has attracted significant interests from different social application tasks, such as helping market promotion [1]–[3], influencing political election [4]–[6], and detecting fake news [7]–[9]. Social network offers a key function named Mention, which allows user to place other users
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