CPMR: context-aware incremental sequential recommendation with pseudo-multi-task learning
The motivations of users to make interactions can be divided into static preference and dynamic interest. To accurately model user representations over time, recent studies in sequential recommendation utilize information propagation and evolution to mine from batches of arriving interactions. Howev...
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Main Authors: | , , , |
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其他作者: | |
格式: | Conference or Workshop Item |
語言: | English |
出版: |
2023
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主題: | |
在線閱讀: | https://hdl.handle.net/10356/170988 |
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機構: | Nanyang Technological University |
語言: | English |