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Content-Based Bipartite User-Image Correlation for Image Recommendation

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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.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant 61702022, in part by Beijing Municipal Education Committee Science Foundation under Grant KM201910005024, in part by Beijing excellent young talent cultivation project under Grant 2017000020124G075, and in part by “Ri xin” Training Programme Foundation for the Talents by Beijing University of Technology.

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Authors and Affiliations

  1. Faculty of Information Technology, Beijing University of Technology, Beijing, China

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

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  1. Meng Jian
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  2. Ting Jia
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  3. Lifang Wu
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  4. Lei Zhang
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  5. Dong Wang
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Correspondence to Lifang Wu.

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Jian, M., Jia, T., Wu, L. et al. Content-Based Bipartite User-Image Correlation for Image Recommendation. Neural Process Lett 52, 1445–1459 (2020). https://doi.org/10.1007/s11063-020-10317-5

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