The popularity of online social curation networks takes benefits from its convenience to retrieve, collect, sort and share multimedia contents among users. With increasing content and user intent gap, effective recommendation becomes highly desirable for its further development. In this paper, we propose a content-based bipartite graph for image recommendation in social curation networks. Bipartite graph employs given sparse user-image interactions to infer user-image correlation for recommendation. Beside given user-image interactions, the user interacted visual content also reveals valuable user preferences. Visual content is embedded into the bipartite graph to extend the correlation density and the recommendation scope simultaneously. Furthermore, the content similarity is employed for recommendation reranking to improve the visual quality of recommended images. Experimental results demonstrate that the proposed method enhances the recommendation ability of the bipartite graph effectively.

在线社交策展网络的普及得益于其在用户之间检索，收集，分类和共享多媒体内容的便利性。随着内容的增加和用户意图的差距，有效的推荐对于其进一步发展变得非常可取。在本文中，我们提出了一种基于内容的二分图，用于社交策展网络中的图像推荐。二分图使用给定的稀疏用户图像交互来推断用户图像相关性以进行推荐。除了给定的用户图像交互之外，用户交互的视觉内容还揭示了有价值的用户偏好。可视内容被嵌入到二分图中，以同时扩展相关密度和推荐范围。此外，内容相似度用于推荐排名，以提高推荐图像的视觉质量。实验结果表明，该方法有效地提高了二部图的推荐能力。