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A Survey of Personalised Image Retrieval and Recommendation

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

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.

Keywords

  • Personalised image retrieval
  • Content-based
  • Collaborative filtering

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This project was supported by NSFC (61272353).

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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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