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• W3024502805 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." @default.
• W3024502805 created "2020-05-21" @default.
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• W3024502805 creator A5071247794 @default.
• W3024502805 date "2020-01-01" @default.
• W3024502805 modified "2023-10-16" @default.
• W3024502805 title "Heterogeneous Information Network Embedding for Mention Recommendation" @default.
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• W3024502805 doi "https://doi.org/10.1109/access.2020.2994313" @default.
• W3024502805 hasPublicationYear "2020" @default.
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