1 code implementation • 26 Jun 2023 • Tatsuya Konishi, Mori Kurokawa, Chihiro Ono, Zixuan Ke, Gyuhak Kim, Bing Liu
Although several techniques have achieved learning with no CF, they attain it by letting each task monopolize a sub-network in a shared network, which seriously limits knowledge transfer (KT) and causes over-consumption of the network capacity, i. e., as more tasks are learned, the performance deteriorates.
1 code implementation • 7 Nov 2022 • Zhi Li, Daichi Amagata, Yihong Zhang, Takahiro Hara, Shuichiro Haruta, Kei Yonekawa, Mori Kurokawa
To address this data sparsity problem, cross-domain recommender systems (CDRSs) exploit the data from an auxiliary source domain to facilitate the recommendation on the sparse target domain.
no code implementations • 29 Sep 2021 • Tatsuya Konishi, Mori Kurokawa, Roberto Legaspi, Chihiro Ono, Zixuan Ke, Gyuhak Kim, Bing Liu
The goal of this work is to endow such systems with the additional ability to transfer knowledge among tasks when the tasks are similar and have shared knowledge to achieve higher accuracy.