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首页> 外文会议>International Conference on Advanced Cloud and Big Data >A Topic Community-Based Method for Friend Recommendation in Online Social Networks via Joint Nonnegative Matrix Factorization
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A Topic Community-Based Method for Friend Recommendation in Online Social Networks via Joint Nonnegative Matrix Factorization

机译：基于主题的社区基于社区的朋友推荐，通过联合非负矩阵分解在线社交网络中的朋友推荐

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摘要

Online social networks (OSN) have become more and more popular and have accumulated a great many users. Friend recommendation can help users discover their interested friends and alleviate the problem of information overload. However, most of existing recommendation methods only consider user link or content information and hence are not effective enough to provide high quality recommendations. In this paper, we propose a topic community-based method via nonnegative matrix factorization (NMF). This method first applies joint NMF model to mine topic community existing in OSN by combing link and content information. Then it makes friend recommendation based on topic community. Experiments show that our method can reflect user preferences on friend selection more appropriately and has better recommendation performance than traditional methods. Moreover, our application case also demonstrates that it can obviously improve friend recommendation service in the real world OSN.
机译：在线社交网络（OSN）变得越来越受欢迎，并积累了很多用户。朋友推荐可以帮助用户发现他们感兴趣的朋友并减轻信息过载问题。但是，大多数现有推荐方法只考虑用户链接或内容信息，因此不足以提供高质量的建议。在本文中，我们通过非环境矩阵分解（NMF）提出了基于社区的基于社区的方法。该方法首先通过梳理链路和内容信息，将联合NMF模型应用于OSN中存在的挖掘主题社区。然后它使基于主题社区的朋友推荐。实验表明，我们的方法可以更适当地反映用户选择的用户偏好，并且具有比传统方法更好的推荐性能。此外，我们的申请案也表明它可以明显改善现实世界OSN中的朋友推荐服务。

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