But, with expansion of E-commerce system's size, collaborative filtering approach suffer from many challenges, for instance, quality of recommendations, scalability, sparsity, cold-start problem.
电子商务系统规模的日益扩大，协同过滤推荐方法也面临诸多挑战：推荐质量、可扩展性、数据稀疏性、冷开始问题等等。
In order to evaluate our new collaborative filtering algorithm and combined approach, we have developed a Prototype System for Chinese computer science literature automatic filtering.
为了对我们提出的改进的协作过滤算法和结合过滤方法进行评价，我们研制了一个中文计算机科技文献自动过滤原型系统。
Compared traditional collaborative filtering method, the experimental results show that our approach can find a solution to the problem of new item recommendation effectively.
实验结果表明：基于项目矩阵划分的协同过滤算法有效地解决新项目推荐困难的问题，显示出了比传统推荐算法更好的推荐质量和扩展性。
Moreover, compared traditional collaborative filtering method, the experimental results show the effectiveness and efficiency of our approach.
实验结果表明：基于稀疏矩阵划分的个性化推荐算法在算法性能上优于传统协同过滤算法。
Moreover, compared traditional collaborative filtering method, the experimental results show the effectiveness and efficiency of our approach.
实验结果表明：基于稀疏矩阵划分的个性化推荐算法在算法性能上优于传统协同过滤算法。
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