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- W2897040023 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." @default.
- W2897040023 created "2018-10-26" @default.
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- W2897040023 date "2018-01-01" @default.
- W2897040023 modified "2023-09-22" @default.
- W2897040023 title "Asynchronous Bi-clustering for Image Recommendation" @default.
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- W2897040023 doi "https://doi.org/10.1007/978-981-13-2922-7_28" @default.
- W2897040023 hasPublicationYear "2018" @default.
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