数据稀缺性问题是协同过滤技术面临的主要挑战。
Data sparsity problem is a potential challenge of collaborative filtering.
协同过滤技术可以通过分析客户群共同的消费品味来形成推荐。
Collaborative Filtering (CF) is used for forming recommendation by analyzing the common "taste" Shared by a group of customers.
协同过滤技术在面对当前的挑战时暴露出许多有待解决的瓶颈问题。
Collaborative filtering technology reveals a number of bottlenecks to be addressed in the face of current challenges.
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.
协同过滤技术分为基于内存和基于模型两种，前者的推荐准确度更高，但可扩展性比后者低。
Collaborative filtering can be divided into memory based and model based. The former is more accurate while the latter performs better in scalability.
协同过滤技术在智能搜索引擎中起到了重要作用，它的核心思想是用户会倾向于利用具有相似意向的用户群产品。
Collaborative filtering technology played an important role in the intelligent search engines, and its cote idea is the users tend to use like - minded user group products.
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.
其中，个性化推荐系统中的协同过滤推荐是迄今为止应用最广泛、最成功的推荐技术。
The collaborative filtering for the personalized recommendation is by far the most widely used and the most successful personalized recommender technology.
协同过滤推荐算法是在电子商务推荐系统中最成功的技术之一。
Collaborative filtering recommendation algorithm is one of the most successful technologies in thee-commerce recommendation system.
协同过滤是目前应用较为成功的信息推送技术，但也遇到了数据稀疏性、冷启动等种种问题。
Collaborative filtering is a successful technology in information push, but this method has encountered data sparse, cold start and other issues.
协同过滤是个性化推荐系统中应用最广泛和最成功的推荐技术，但是它也面临着推荐准确度和可扩展性两大挑战。
Collaborative filtering is the most widely used and successful technology for personalized recommender systems. However it faces challenges of scalability and recommendation accuracy.
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.
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.
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.
应用推荐