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- W4312589670 abstract "Reading newly-published papers in time is important for researchers since these papers provide the latest research findings. However, it is challenging to retrieve newly-published papers through common query-based search engines because the papers lacking sufficient citations and links are usually ranked too low in the search list. To this end, we design a time-aware joint model to infer users’ preference for the newly-published papers with the help of subsidiary relations of social and article linkages. The temporal preference of researchers for articles is jointly modeled with social and article relations by a group of matrices sharing common dimensions of researchers and articles. A joint multi-relational factorization algorithm is devised to approximate the latent factor matrices along with a temporal recommendation algorithm to predict the personalized new referential papers based on these factor matrices. The experimental results on real-world datasets show that the proposed model outperforms the state-of-the-art methods." @default.
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- W4312589670 date "2022-01-01" @default.
- W4312589670 modified "2023-09-25" @default.
- W4312589670 title "Newly-Published Paper Recommendation With a Joint Multi-Relational Model" @default.
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- W4312589670 doi "https://doi.org/10.1109/access.2022.3223679" @default.
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