基于LDA的隐式标签协同过滤推荐算法

Implicit Tag Collaborative Filtering Recommendation Algorithm Based on LDA

固定标签协同过滤推荐算法,未充分考虑标签因子的多样化,主要依靠人工标记,扩展性不强,主观因素多。本文从用户的喜好特征因素角度出发,在固定标签协同过滤推荐算法的基础上,提出一种隐式标签协同过滤推荐算法。该算法利用LDA主题模型生成项目文本的隐式标签,得到项目-标签特征权重,根据算法性能优化的要求选择标签数量,将项目-标签矩阵与用户评分矩阵结合得到用户对标签的偏好矩阵,最后通过协同过滤算法产生推荐。实验结果表明,本文提出的基于LDA的隐式标签协同过滤推荐算法缓解了数据稀疏性问题,项目推荐的召回率、准确度和F1值有较大提升。

The fixed tag collaborative filtering recommendation algorithm does not fully consider the diversity of tag factors,and mainly relies on manual tagging,which is not scalable and has many subjective factors.In this paper,based on the fixed tag collaborative filtering recommendation algorithm,an implicit tag collaborative filtering recommendation algorithm is proposed from the perspective of user preferences.This algorithm uses LDA topic model to generate implicit tags of item text,and obtains item-tag feature weights.The number of tags is selected according to the requirements of algorithm performance optimization,and the user’s preference matrix for tags is obtained by combining the item-tag matrix with the user scoring matrix.Finally,the recommendation is generated by collaborative filtering algorithm.The experimental results show that the user-based LDA tag collaborative filtering algorithm proposed in this paper alleviates the problem of data sparsity,and greatly improves the recall rate,accuracy and F1 value of item recommendation.