Traditional user modeling methods for news recommendation struggle to deeply analyze the complex semantics of news and the genuine needs of users.To address this issue,this paper first proposes a knowledge-enhanced news model-ing approach,which obtains news document representations through an entity representation layer,a context embedding lay-er,and an attention aggregation layer.Based on this,a fine-grained user modeling method based on knowledge-enhanced documents is proposed,utilizing long document modeling techniques to concatenate knowledge-enhanced news documents in-to a long document.Fine-grained user representations are obtained by capturing word-level interactions between documents,while coarse-grained user representations are derived from capturing entity interactions within documents.The final user representation is aggregated from both coarse-grained and fine-grained user representations.Experimental results show that the proposed news modeling method outperforms baseline models in terms of AUC and NDCG@10 metrics,with the user modeling method based on this approach achieving at least a 2.51％improvement in AUC and at least a 4.75％improvement in NDCG@10.
国家科技期刊平台
NSTL
万方数据
