考虑用户活跃度和项目流行度的基于项目最近邻的协同过滤算法

项目相关性度量是基于项目最近邻的协同过滤算法的关键。已有的项目相关性度量方法在数据集稀疏或推荐低流行度产品时会面临较大挑战,因此提出一种考虑用户活跃度和项目流行度的基于项目最近邻的协同过滤算法。该算法在度量两个项目的相关性时,若有记录只对两个项目之一有评分,则利用该记录所对应的评分用户的活跃度和被评价项目的流行度进行相关性惩罚,从而提高数据稀疏环境下低流行度产品被推荐的概率。实验表明,所提算法在保证评分预测精度的情况下提升了推荐结果的多样性和新颖性。

Item-based Collaborative Filtering Algorithm Integrating User Activity and Item Popularity

Item correlation computation is the most critical component in item- based collaborative filtering algorithm.The traditional correlation computation scheme can be challenged by the sparse data set and the situation of recommending unpopular products.In this paper,a novel item- based collaborative filtering algorithm that incorporates the activity of users and popularity of items was proposed.The proposed computation scheme decreases the correlation between items using the activity of users and popularity of items in those rating records where only one item is rated.In this way,the unpopular products can be recommended to users in the sparse data.Experimental evaluation shows that the diversity and novelty of the recommendation list can be improved while maintaining the prediction accuracy.