Data sparsity problem is a potential challenge of collaborative filtering.
数据稀缺性问题是协同过滤技术面临的主要挑战。
Aiming at the problem of data sparsity for collaborative filtering, a novel rough set-based collaborative filtering algorithm is proposed.
针对协同过滤中的数据稀疏问题，提出了一种基于粗集的协同过滤算法。
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。
Translation template can solve the problem of data sparsity, large storage space and low matching precision of examples.
利用翻译模板可以有效的解决翻译实例的数据稀疏问题、简化实例库的规模并提高实例匹配的精确率。
The main disadvantage of the way is the problem of data sparsity.
对于半监督方法，目前存在的一个严重的问题是数据稀疏问题。
The sparsity and the problem of the curse of dimensionality of high-dimensional data, make the most of traditional clustering algorithms lose their action in high-dimensional space.
高维数据的稀疏性和“维灾”问题使得多数传统聚类算法失去作用，因此研究高维数据集的聚类算法己成为当前的一个热点。
The improved methods can effectively alleviate the problem of sparsity and improve the quality of recommendation system.
本文提出的算法能够有效缓解数据稀疏性问题，提高推荐系统的推荐质量。
The improved methods can effectively alleviate the problem of sparsity and improve the quality of recommendation system.
本文提出的算法能够有效缓解数据稀疏性问题，提高推荐系统的推荐质量。
应用推荐