Cross-domain recommendation (CDR) uses auxiliary interactions from a source domain to mitigate data sparsity in a target domain. However, implicit feedback in the source domain may include false positives that distort user representations and transfer misleading preferences to the target domain. We refer to this proble...
Guo-Hang Zeng, Jie Lu, Guang-Quan Zhang· Proceedings of the 20th ACM...· 0 citations
Cross-domain recommendation (CDR) uses auxiliary interactions from a source domain to mitigate data sparsity in a target domain. However, implicit feedback in the source domain may include false positives that distort user representations and transfer misleading preferences to the target domain. We refer to this proble...
Guo-Hang Zeng, Jie Lu, Guang-Quan Zhang· Proceedings of the 20th ACM...· 0 citations
This work is the first to reformulate the recommendation denoising problem into a Partial Label Learning (PLL) task, and innovatively leverages PLL paradigms to directly resolve ambiguous implicit feedback, effectively recovering clean signals from noisy candidate sets.
Huilin Chen, Jie Lu, Kezhi Lu et al.· Annual International ACM SIG...· 0 citations
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