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.
通过为每位顾客建立个性化的购物体验，推荐算法提供了一种有效的定向营销形式。
Recommendation algorithms provide an effective form of targeted marketing by creating a personalized shopping experience for each customer.
完成了该算法的实现，并集成到现有的CADAL数字图书馆门户个性化模块中，达到了较理想的推荐效果。
Forth, complete the implementation of the algorithm, integrate it into the existing CADAL Digital Library Portal personalization module and achieve better recommendation effectiveness.
实验结果表明，该算法可以有效地提高数字图书馆个性化推荐系统的可扩展性及推荐准确度。
The experimental results demonstrate that the algorithm can effectively improve scalability and accuracy of the digital library of personalized recommendation system.
众多个性化推荐技术中协同过滤可谓一枝独秀，该算法引领了当今各大电子商务平台的推荐系统的发展趋势。
Collaborative filtering is thriving among lots of personalized recommendation technology which leads the recommendation system trends of major e-commerce platforms.
众多个性化推荐技术中协同过滤可谓一枝独秀，该算法引领了当今各大电子商务平台的推荐系统的发展趋势。
Collaborative filtering is thriving among lots of personalized recommendation technology which leads the recommendation system trends of major e-commerce platforms.
应用推荐