Fusing time-aware and multi-interest extraction network for sequential recommendation
To address the challenges of temporal dynamics and multi-interest modeling in sequence recommendation tasks,this paper proposes a time-aware item embedding method to learn the temporal correlations among items.Building upon this,we introduce a sequence recommendation approach called time-aware multi-interest sequence recommendation(TMI-SA),which integrates time awareness with multi-interest extraction networks.Firstly,TMISA employs a self-attention-based sequence recommendation model as a local feature learning module to capture dynamic user preferences within behav-ior sequences.Secondly,it models global user preferences through a multi-interest extraction network.Finally,a gate aggre-gation module dynamically merges local and global feature representations to generate the ultimate user preference represen-tation.Experimental results demonstrate that the TMISA model exhibits outstanding performance across five real-world rec-ommendation datasets,surpassing multiple state-of-the-art baseline models.
国家科技期刊平台
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