关键词: 固定标签, 协同过滤, LDA主题模型, 隐式标签, 算法改进

Abstract: 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.

Key words: fixed label, collaborative filtering, LDA theme model, implicit label, algorithm improvement