Abstract: A Web news recommendation method based on multiple topic tracking was proposed to improve the precision of recommendation. The proposed algorithm used multiple user profiles to represent user's interests in different topics, and dynamically updated user's profile to reflect the changing of user's interests. The central recommendation algorithm was implemented, and experiments on Reuters Corpus Volume 1 were carried out. The experimental results show that the proposed algorithms can effectively improve the precision of recommendation.

Key words: news recommendation, multiple topic, user profile

摘要： 针对网络新闻推荐系统推荐准确率偏低的问题，提出一种基于多主题追踪的网络新闻推荐算法。基于多主题追踪的推荐算法采用多个用户模型表示用户对不同主题的兴趣，并动态更新用户模型以动态反映用户的兴趣变化。实现了网络新闻推荐系统的核心推荐算法，并在标准路透社新闻数据集(RCV1)上验证了算法的有效性，有效提升了新闻推荐的准确率。

关键词: 新闻推荐, 多主题, 用户模型