LSIHS: Long and Short-term Recommendation Model Integrated with Hierarchical Structure
Pages 228 - 235
Abstract
Personalized news recommendation is essential to relieve information overload and improve users’ experience. The existing recommendation models would only be concerned with the users’ single representations. In fact, everyone has some long-term interests and short-term interests, and these interests are diverse and multi-grained. In this paper, we propose a Long and Short-term Recommendation Model Integrated with Hierarchical Structure (LSIHS). The model uses a three-level hierarchical structure to capture multi-grained long-term interests, uses the Gated Recurrent Unit (GRU) to capture the short-term interests, and uses the long-term interests to initialize the GRU. We do some experiments on three datasets. The results show that the model effectively improves the performance of personalized recommendation.
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- LSIHS: Long and Short-term Recommendation Model Integrated with Hierarchical Structure
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Published In
September 2022
1221 pages
ISBN:9781450396899
DOI:10.1145/3573942
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 16 May 2023
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AIPR 2022
AIPR 2022: 2022 5th International Conference on Artificial Intelligence and Pattern Recognition
September 23 - 25, 2022
Xiamen, China
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