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Flickr group recommendation with auxiliary information in heterogeneous information networks

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Abstract

Over the past few years, the appropriate utilization of user communities or image groups in social networks (i.e., Flickr or Facebook) has drawn a great deal of attention. In this paper, we are particularly interested in recommending preferred groups to users who may favor according to auxiliary information. In real world, the images captured by mobile equipments explicitly record a lot of contextual information (e.g., locations) about users generating images. Meanwhile, several words are employed to describe the particular theme of each group (e.g., “Dogs for Fun Photos” image group in Flickr), and the words may mention particular entities as well as their belonging categories (e.g., “Animal”). In fact, the group recommendation can be conducted in heterogeneous information networks, where informative cues are in general multi-typed. Motivated by the assumption that the auxiliary information (visual features of images, mobile contextual information and entity-category information of groups in this paper) in heterogeneous information networks will boost the performance of the group recommendation, this paper proposes to combine auxiliary information with implicit user feedback for group recommendation. In general, the group recommendation in this paper is formulated as a non-negative matrix factorization (NMF) method regularized with user–user similarity via visual features and heterogeneous information networks. Experiments show that our proposed approach outperforms other counterpart recommendation approaches.

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Notes

  1. https://github.com/yueyangzju/FDAI.

  2. https://www.flickr.com/services/api/.

  3. http://en.wikipedia.org/wiki/.

  4. http://caffe.berkeleyvision.org/.

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Acknowledgments

This work is supported by the National Basic Research Program of China under Grant (2015CB352302), Zhejiang Provincial Natural Science Foundation of China (LQ14F010004) and the Fundamental Research Funds for the Central Universities (2015XZZX005-06).

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Authors and Affiliations

  1. Key Laboratory of Big Data Intelligent Computing of Zhejiang Province, Innovation Joint Research Center for ICPS, College of Computer Science and Technology, Zhejiang University, Hangzhou, China

    Yueyang Wang, Yuanfang Xia, Siliang Tang, Fei Wu & Yueting Zhuang

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  1. Yueyang Wang
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  2. Yuanfang Xia
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  3. Siliang Tang
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  4. Fei Wu
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  5. Yueting Zhuang
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Corresponding author

Correspondence to Yueyang Wang.

Additional information

This is an extension to “Flickr Group Recommendation via Heterogeneous Information Network” in ICIMCS 2015 [1].

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Wang, Y., Xia, Y., Tang, S. et al. Flickr group recommendation with auxiliary information in heterogeneous information networks. Multimedia Systems 23, 703–712 (2017). https://doi.org/10.1007/s00530-015-0502-5

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