Content-Based Bipartite User-Image Correlation for Image Recommendation

Meng Jian, Ting Jia, Lifang Wu, Lei Zhang, Dong Wang

Abstract: 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.