协同过滤推荐技术(Collaborative filtering approach)是目前研究最多、应用最广的电子商务推荐技术。它是基于邻居用户的资料得到对目标用户的推荐，推荐的个性化程度高。
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受电子商务研究领域中相关研究成果启发，我们尝试将协同过滤推荐技术引入学习资源的个性化推荐研究中。
Be inspired by the research achievement in e-commerce fields, we try to introduce the collaborative filtering technology into research of personalized recommendation of learning resources.
但是随着用户数量和系统规模的不断扩大，协同过滤推荐技术将面临严重的数据稀疏性、超高维、冷启动和实时推荐等方面的挑战。
However, collaborative filtering has got challenges, such as data sparsity, high dimensions, cold start, and real-time recommendation issues with the fast growth in the amount of users and items.
该模型的建立对于缓解协同过滤技术中存在的稀疏性问题、推荐的实时性问题有很大的帮助。
This model of collaborative filtering technology is great help in the mitigation of existing sparse problems and recommendation in time.
应用推荐