何婵军,汪洋,钟毓霖,吴尚猷

• 1:湖北省水利水电规划勘测设计院有限公司

摘要(Abstract)：

针对现有兴趣点推荐模型往往忽视轨迹局部序列特征及高访问兴趣点影响力，导致用户轨迹信息挖掘不充分、下一个兴趣点推荐精度低的问题，提出了一种融合局部与全局序列建模及兴趣点影响力加权的深度神经网络模型，旨在同步捕捉用户轨迹的多粒度序列特征和兴趣点的热点特征。通过在多个真实签到数据集上实验，与协同过滤、贝叶斯个性化排序、CBOW、RKGE和Caser等基线模型相比，本文提出的模型在Pred@5、Pred@10和Pred@15等评价指标上均取得了最优性能，验证了模型的有效性。本文提出的融合序列特征与热点特征的深度神经网络模型，能够更全面地挖掘用户行为信息，显著提升兴趣点推荐的准确性。

关键词(KeyWords)： 兴趣点推荐;神经网络;卷积模型;序列特征

Abstract:

Keywords:

基金项目(Foundation):

作者(Author): 何婵军,汪洋,钟毓霖,吴尚猷

DOI: 10.16251/j.cnki.1009-2307.2026.11.012

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