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首页> 外文期刊>International Journal of Knowledge-Based in Intelligent Engineering Systems >Recommender system using item based collaborative filtering (CF) and K-means
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Recommender system using item based collaborative filtering (CF) and K-means

机译：使用基于项目的协作过滤（CF）和K-means的推荐系统

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摘要

The heightening in the available information in the form of digital data and the number of users on the Internet have engendered a challenge of overburden of data which obstructs access to interested item on the Internet timely. There are many information retrieval systems which try to solve the problem of information overloading but in their cases prioritization and personalization of information were absent. The main aim is to develop a recommender system using item based collaborative filtering technique and K-means. The most popular algorithm in the recommender system’s field is the collaborative filtering technique. Recommender systems are the filtering systems for information that concerned with the problem of information overburden by filtering essential information fragment out of enormous dynamically promoted information according to person’s attentiveness, taste and distinguished behavior about them. We are considering m users, n items (in numbers) and presenting a model to fabricate a recommendation for the mobile user by a new approach.
机译：数字数据形式的可用信息的增加以及Internet上的用户数量带来了数据过载的挑战，这阻碍了及时访问Internet上感兴趣的项目。有许多信息检索系统试图解决信息超载的问题，但在这种情况下，缺少信息的优先级和个性化设置。主要目的是使用基于项目的协同过滤技术和K-means开发推荐系统。推荐系统中最流行的算法是协作过滤技术。推荐系统是一种信息过滤系统，它根据人的注意力，品味和与众不同的行为，从巨大的动态提升信息中过滤掉重要信息片段，从而解决与信息过载有关的信息。我们正在考虑m个用户，n个项目（数量），并提出一种模型，以一种新方法为移动用户编制推荐。

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