关键词: 推荐系统, 协同过滤, 数据预测, 数据稀疏性, recommendation system, collaborative filtering, data prediction, data sparsity

Abstract: Collaborative filtering has been successfully applied to various recommendation systems. Unfortunately, with the tremendous growth in the amount of items and users, the lack of original rating poses some key challenges for recommendation quality. To address this problem, the paper explored a new hybrid CF approach which improved the traditional similarity coefficient computation combining the portal website natural structure, then the missing preference values were predicted with new similarity based on the item-based CF. The improvements made an increasing intercross of the rating matrix for alleviating sparsity. The experimental results show the proposed algorithm outperforms the traditional CF, and it can recommend potential preference pages for visitors.