计算机应用与软件2025，Vol.42Issue(4)：326-334,9.DOI:10.3969/j.issn.1000-386x.2025.04.046
融合Node2Vec和负反馈强化学习的商品推荐算法
RECOMMENDATION METHOD WITH NODE2VEC AND NEGATIVE FEEDBACK REINFORCEMENT LEARNING
摘要
Abstract
The long-tail problem is very common in recommendation system.It leads to recommending few and homogeneous products.We propose a new recommendation algorithm named GES4RL,which combines graph embedding with side information and reinforcement learning to solve long-tail problem.GES4RL is based on Node2Vec and negative feedback reinforcement learning.It constructs a weighted directed graph of product propagation and uses Node2Vec to learn the embedding of products.We used gated recurrent unit(GRU)to learn user's dynamic preferences and designed a negative feedback reinforcement learning model to generate the best recommendation strategy for long-tail products.Experimental results on User Behavior Dataset provided by TianChi show that the algorithm improves the diversity and hit rate of recommendations significantly.关键词
推荐系统/长尾问题/有偏随机游走/深度强化学习/门控循环单元Key words
Recommendation system/Long-tail problem/Node2Vec/Deep reinforcement learning/Gated recurrent unit分类
信息技术与安全科学引用本文复制引用
陶文慧..融合Node2Vec和负反馈强化学习的商品推荐算法[J].计算机应用与软件,2025,42(4):326-334,9.
