基于社区网络内容的个性化推荐算法研究

摘要

Based on the issue that how to acquire the interesting information for users quickly and effectively from massive data, this paper proposed a personalized recommendation algorithm based on content in social network. On the basis of obtaining user clusters with common interest, firstly used content similarity algorithm to mine content recommendation items based on social network logs, then obtained new content recommendation items based on user interest. The experimental results show that the algorithm can not only improve the accuracy of the content recommendation, but also extend the coverage of content.%针对用户在社区网络中面对海量的信息和资源,如何快速便捷地获得自己感兴趣内容的问题,提出一种基于社区网络内容的个性化推荐算法.在得到相同兴趣用户聚类的基础上,该算法首先通过用户访问日志信息挖掘相似内容推荐项,然后根据用户兴趣挖掘新的内容推荐项.实验结果表明,该算法不仅提高了内容推荐精度,而且扩展了内容覆盖面.