摘要
为更好地满足了推荐系统中用户个性化推荐的需求,提高推荐系统的性能。研究了用户兴趣模型,提出了一种用户兴趣模型自动更新的方法,在数据采集过程中,通过对隐性数据的采集,动态地更新用户模型;模型使用向量空间模型的表示方法。实验结果表明,新的模型提高了计算用户最近邻居的准确性,算法在不同推荐范围都用良好的表现,并具有很好的耐久性。
To better meet the user＇s individuation demand in recommendation system and to raise the performance of recommendation system,this paper studyed the user interest model,then provide an algorithm of updating the interest model.At the data acquisition section,via gather the recessive data,model get the up to date data to fit the updating algorithm.The model is expressed by Vector Space Model. The experimental results suggest that this algorithm has upstanding effect in some different recommend scope and has exceUent durability.
出处
《微计算机信息》
2012年第3期133-134,175,共3页
Control & Automation
关键词
推荐系统
自适应
推荐算法
用户兴趣模型
recommendation system
seff-adaption
recommendation algorithm
user interest model
