计算技术与自动化2024，Vol.43Issue(4)：161-166,6.DOI:10.16339/j.cnki.jsjsyzdh.202404026
基于知识增强的细粒度个性化新闻推荐用户建模
User Modeling for Fine-Grained Personalized News Recommendation Based on Knowledge Enhancement
熊晓波 1方文涛2
作者信息
- 1. 湖南省高速公路集团有限公司,湖南长沙 410153
- 2. 中联重科股份有限公司,湖南长沙 410013
- 折叠
摘要
Abstract
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.关键词
新闻建模/用户建模/知识图谱/细粒度/个性化新闻推荐Key words
news modeling/user modeling/knowledge graph/fine-grained/personalized news recommendation分类
信息技术与安全科学引用本文复制引用
熊晓波,方文涛..基于知识增强的细粒度个性化新闻推荐用户建模[J].计算技术与自动化,2024,43(4):161-166,6.
