结合项目流行度加权的协同过滤推荐算法

针对传统协同过滤算法中存在的流行度偏差问题，提出一种结合项目流行度加权的协同过滤推荐算法。在项目协同过滤算法的基础上，分析项目流行度和流行度差异对相似度的影响；设置流行度阈值，对大于该阈值的流行项目设计惩罚权重，降低其对项目间相似度的贡献。通过在MovieLens 1M和Epinion数据集上进行实验验证和对比，结果表明，所提算法的预测准确度和覆盖率均优于传统算法，有效提高了推荐的多样性和新颖性，一定程度上缓解了流行度偏差问题。

Collaborative filtering recommendation algorithm combined with item popularity weighting

Aiming at the popularity bias problem in traditional collaborative filtering algorithms, this paper proposed a collaborative filtering recommendation algorithm combined with item popularity weighting. On the basis of the item collaborative filtering algorithm, it analyzed the influence of item popularity and popularity difference between items on similarity. This algorithm used the item popularity and popularity difference to set the penalty weight functions and adjusted the similarity between popularity items when the item popularity was greater than the threshold. The experiments on the MovieLens 1M and Epinion datasets show that the proposed algorithm has better prediction accuracy and coverage rate than traditional algorithms, which effectively improves the diversity and novelty of recommendations, and alleviates the popularity bias problem.