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

作　　者：陶文慧 Tao Wenhui(Software School,Fudan University,Shanghai 200438,China)

机构地区：[1]复旦大学软件学院,上海200438

出　　处：《计算机应用与软件》2025年第4期326-334,共9页Computer Applications and Software

摘　　要：目前推荐系统普遍存在长尾问题,导致商品推荐覆盖率低、多样性差,为此提出一种融合有偏随机游走(Node2Vec)和负反馈强化学习的商品推荐算法GES4RL(Graph Embedding with Side Information for Reinforcement Learning)。对商品传播的有向加权图使用Node2Vec算法学习商品的编码表示;引入门控循环单元(GRU)对用户偏好的动态情况进行建模,并使用基于负反馈强化学习模型计算出长尾商品的最佳推荐策略。在TianChi电商数据集上的实验表明,该算法显著提高了商品推荐的多样性和命中率。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 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]