Content-Based Bipartite User-Image Correlation for Image Recommendation

被引：0
作者：
Meng Jian
Ting Jia
Lifang Wu
Lei Zhang
Dong Wang
机构：
[1] Beijing University of Technology,Faculty of Information Technology
来源：
Neural Processing Letters | 2020年 / 52卷
关键词：
Bipartite graph; Visual correlation; Personalized recommendation; Social multimedia network;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
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.
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页码：1445 / 1459
页数：14
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