Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

2-2022

Abstract

In many visually-oriented applications, users can select and group images that they find interesting into coherent clusters. For instance, we encounter these in the form of hashtags on Instagram, galleries on Flickr, or boards on Pinterest. The selection and coherence of such user-curated visual clusters arise from a user’s preference for a certain type of content as well as her own perception of which images are similar and thus belong to a cluster. We seek to model such curation behaviors towards supporting users in their future activities such as expanding existing clusters or discovering new clusters altogether. This paper proposes a framework, namely Collaborative Curating that jointly models the interrelated modalities of preference expression and similarity perception. Extensive experiments on real-world datasets from a visual curating platform show that the proposed framework significantly outperforms baselines focusing on either clustering behaviors or preferences alone.

Keywords

Collaborative curating, Visual curation, Visual discovery

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Science and Engineering

Publication

WSDM '22: Proceedings of the 15th ACM International Conference on Web Search and Data Mining, Virtual, February 21-25

First Page

544

Last Page

552

ISBN

9781450391320

Identifier

10.1145/3488560.3498504

Publisher

ACM

City or Country

New York

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1145/3488560.3498504

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