Nowadays, many people use social media to communicate with others, share their interests and obtain information. As the performance of the embedded cameras on mobile phones improve, image-sharing social media became a popular tool for people to communicate with others and share their interests, which yields vast amount of data related to the users’ interests. However, only few studies pay attention to analyze data in image-sharing social media and utilize it to perform appropriate services, such as recommendation. We propose a framework to discover user interests using the Latent Dirichlet Allocation (LDA) based topic model and to recommend protentional friends and POIs related to the target user’s interests. To do this, we devise the advanced LDA based topic model which can be utilized in image-sharing social media by exploiting both textual features and visual features. In addition, the novel method to discover user interest is proposed by generating topic graph to represent the user interest as graph-shape, which is an effective way to completely describe the user interest as explicit form. Lastly, we propose a method to recommend POIs and potential friends to the target user by calculating graph similarity between topic graphs. To demonstrate the superiority of our framework, we collected real data from image-sharing social media and conducted comparison experiments with state-of-the-art methods.

如今，许多人使用社交媒体与他人交流，分享自己的兴趣并获取信息。随着移动电话上嵌入式相机性能的提高，图像共享社交媒体已成为人们与他人交流和分享他们的兴趣的流行工具，这产生了与用户兴趣有关的大量数据。但是，只有很少的研究关注分析共享图像的社交媒体中的数据并将其用于执行适当的服务，例如推荐。我们提出了一个框架，该框架使用基于潜在狄利克雷分配（LDA）的主题模型发现用户兴趣，并推荐与目标用户兴趣相关的临时朋友和POI。去做这个，我们设计了基于LDA的高级主题模型，该模型可通过利用文本特征和视觉特征在共享图像的社交媒体中使用。此外，提出了一种通过生成主题图以图形形式表示用户兴趣的发现用户兴趣的新方法，这是一种将用户兴趣完全描述为显式形式的有效方法。最后，我们提出了一种通过计算主题图之间的图相似度向目标用户推荐POI和潜在朋友的方法。为了展示我们框架的优越性，我们从共享图像的社交媒体中收集了真实数据，并使用最先进的方法进行了对比实验。这是将用户兴趣完整描述为显式形式的有效方法。最后，我们提出了一种通过计算主题图之间的图相似度向目标用户推荐POI和潜在朋友的方法。为了展示我们框架的优越性，我们从共享图像的社交媒体中收集了真实数据，并使用最先进的方法进行了对比实验。这是将用户兴趣完整描述为显式形式的有效方法。最后，我们提出了一种通过计算主题图之间的图相似度向目标用户推荐POI和潜在朋友的方法。为了展示我们框架的优越性，我们从共享图像的社交媒体中收集了真实数据，并使用最先进的方法进行了对比实验。