摘要
基于项目的协同过滤(item-based collaborative filtering,IBCF)算法推荐精度高,实现简单,易于用于实际系统,然而因Item向量过长,计算相似性十分耗时。针对这一问题,从Item向量过长入手,提出了一种均模型表示Item向量的方法,缩短计算相似性的时间。在Movie Lens数据集上进行对比,实验表明,该算法在推荐精度基本保证的情况下,能有效缩短计算时间,降低时间复杂度。此外,本文还指出上述优化相似性计算方法可进一步优化来提高推荐精度和满足实际应用要求。
The item-based collaborative filtering algorithm （ IBCF）, a recommendation algorithm with high precision, simple and easy to use in actual system, is widely used in the field of recommendation systems. But it meets a higher computational time complexity for similar calculation because of the long length of item vector. In this paper, a sampled approach firstly is suggested to represent an item vector called mean model item vector representation through analyzing theory of IBCF algorithm, to solve the problem of the long length of item vector and cut down the computational time. Experiments using Movie Lens datasets show that the algorithm is very efficient to cut down the computational time on the premise of accuracy. Furthermore, some right sampling methods can be used to optimize the calculation method of similarity in order to meet practical application requirement.
出处
《山东大学学报（理学版）》
CAS
CSCD
北大核心
2013年第11期105-110,共6页
Journal of Shandong University(Natural Science)
基金
山西省自然科学基金资助项目(200821024)
关键词
相似性计算
均模型
推荐系统
基于Item的协同过滤
similarity computing
mean model
recommendation system
item-based collaborative filtering
