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A Survey of Personalised Image Retrieval and Recommendation

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Theoretical Computer Science (NCTCS 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 768))

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Abstract

With the advent of web2.0 era, it has been becoming increasingly easy to create and share Internet content. Plenty of pictures are uploaded to the Internet every day. A primary challenge against traditional image retrieval technologies is how to help users quickly discover the images they need. Personalised image retrieval is a new trend in the field of image retrieval. It not only improves the accuracy of the existing retrieval systems, but also better meets the users’ needs. Personalised image retrieval and recommendation (PIRR) can be grouped into two main categories, content-based PIRR and collaborative filtering (CF)-based PIRR. This paper first summarises the development of image retrieval and introduces different image retrieval solutions. Then the key technologies of content-based PIRR are analysed from three aspects, user interest acquisition, user interest representation and personalised implementation. Different techniques are compared and analysed. Regarding CF-based PIRR, the user-based, item-based and model-based CF-based PIRR are introduced and compared. At the end of the paper, we compare and summarise content-based PIRR and CF-based PIRR.

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Acknowledgements

This project was supported by NSFC (61272353).

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Authors and Affiliations

  1. Beijing Jiaotong University, Beijing, 100044, China

    Zhenyan Ji, Weina Yao, Huaiyu Pi & Wei Lu

  2. Victoria University, Melbourne, VIC, 3011, Australia

    Jing He

  3. Washington University in St. Louis, St. Louis, MO, 63130, USA

    Haishuai Wang

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  1. Zhenyan Ji
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  2. Weina Yao
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  3. Huaiyu Pi
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  4. Wei Lu
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  5. Jing He
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Correspondence to Zhenyan Ji .

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Editors and Affiliations

  1. The University of Texas at Dallas, Richardson, Texas, USA

    Dingzhu Du

  2. Hefei University of Technology, Hefei, Anhui Province, China

    Lian Li

  3. National University of Defense Technology, Changsha, Hunan, China

    En Zhu

  4. Huazhong University of Science and Technology, Wuhan, Hubei, China

    Kun He

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Ji, Z., Yao, W., Pi, H., Lu, W., He, J., Wang, H. (2017). A Survey of Personalised Image Retrieval and Recommendation. In: Du, D., Li, L., Zhu, E., He, K. (eds) Theoretical Computer Science. NCTCS 2017. Communications in Computer and Information Science, vol 768. Springer, Singapore. https://doi.org/10.1007/978-981-10-6893-5_18

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  • DOI: https://doi.org/10.1007/978-981-10-6893-5_18

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