Newly-published paper recommendation with a joint multi-relational model

Wenjuan Ren*, Dora D. Liu, Qian Li, Usman Naseem

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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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 the factor matrices. The experimental results on real-world datasets show that the proposed model outperforms the state-of-the-art methods.

Original languageEnglish
Pages (from-to)123995-124001
Number of pages7
JournalIEEE Access
Publication statusPublished - 2022
Externally publishedYes

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