摘要
传统的二部图推荐模型只考虑了用户和物品的历史交互行为。为了提供更加准确、多样和可解释的推荐,需要在用户物品交互式建模的基础上充分考虑标签辅助信息及权值的计算方式。文中提出了基于自然语言处理的标签相似性辅助边优化的推荐算法(LWV)。该方法结合用户历史行为和标签辅助信息,通过word2vec在节点间生成新用于节点交互的边并构建边的权重,来更新基础推荐算法的推荐列表。最后,在公开数据集上对文中算法与基准算法在6个公共评测标准进行对比,实验结果表明,LWV更新过的推荐算法相比原算法在准确性、多样性和新颖性方面获得更好平衡。
The traditional bipartite recommendation model only considers the historical interaction behavior of users and items.In order to provide more accurate,diverse and interpretable recommendations,it is necessary to fully consider the label auxiliary information and the calculation method of weights on the basis of user-item interactive modeling.This paper proposed a recommendation algorithm(LWV)based on natural language processing for tag similarity auxiliary edge optimization.This method combined user historical behavior and tag assistance information to generate new edges for node interaction between nodes through word2vec and constructed the weight of the edges to update the recommendation list of the basic recommendation algorithm.A comparison between this algorithm and the benchmark algorithm in six public evaluation standards on the public data set shows that the updated recommendation algorithm of LWV achieves a better balance in terms of accuracy,diversity and novelty than the original algorithm.
作者
蔡彪
陈润
CAI Biao;CHEN Run(College of Information Science and Technology,Chengdu University of Technology,Chengdu 610059,P.R.China)
出处
《重庆大学学报》
EI
CAS
CSCD
北大核心
2020年第11期52-62,共11页
Journal of Chongqing University
基金
国家自然科学基金资助项目(61802034,61701049)
四川省软科学研究项目(2019JDR0117)。
