Abstract
Collaborative filtering (CF) plays a key role in various recommendation systems, but its effectiveness will be limited by the highly sparse user-image click-through data when CF deploys for image recommendation applications. Some existing methods apply clustering techniques to mitigate the sparseness issue. However, there is still a big room to elevate the recommendation performance, because little is known in taking both click-through data and image visual information into account. In this paper, we propose an Asynchronously Bi-Clustering (ABC) CF approach to improve the CF-based image recommendation. Our ABC approach consists of two coupled clustering solutions. Concretely, it first implements image clustering based on image click-through and visual feature, and then conducts user clustering in a low-dimensional subspace spanned by the image clusters. The final recommendation is accomplished based on both user clusters and image clusters by a similarity fusion strategy. An empirical study shows that our ABC approach is beneficial to the CF-based image recommendation, and the proposed scheme is significantly more effective than some existing methods.
Supported by NSFC of China.
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Notes
- 1.
http://www.datatang.com/data/44353. The dataset was firstly used in [17].
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This work is supported by NSFC61671048 and NSFC61672088 and NSFC61790575.
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Jia, Y., Li, Y., Wu, J. (2018). Asynchronous Bi-clustering for Image Recommendation. In: Xu, Z., Gao, X., Miao, Q., Zhang, Y., Bu, J. (eds) Big Data. Big Data 2018. Communications in Computer and Information Science, vol 945. Springer, Singapore. https://doi.org/10.1007/978-981-13-2922-7_28
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DOI: https://doi.org/10.1007/978-981-13-2922-7_28
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