摘要
协同过滤技术 (collaborative filtering)目前被成功地应用于个性化推荐系统中 ,但随着系统规模的扩大 ,它的效能逐渐降低 ,针对它的缺点 ,提出了一种高效的个性化推荐算法 ,它包括维数简化和项集相似性计算两个过程 ,这种算法在提高精确性的基础上减少了计算耗费 ,可以较好地解决应用协同过滤技术的推荐系统所存在的稀疏性、扩展性等问题 。
Collaborative filtering is the most successful technology for building recommendation systems. Unfortunately,the efficiency of these methods decline linearly with the number of users and items .To address these limitations, a high efficient personalization recommendation algorithm is presented, which includes two phases: dimensionality reduction and item-based recommendation methods. This algorithm reduces the computation consumption based on enhancing the accuracy, etc. It may solve questions well such as sparsity, scalability. It can create accurate personalization recommendation quickly.
出处
《计算机研究与发展》
EI
CSCD
北大核心
2002年第8期986-991,共6页
Journal of Computer Research and Development
基金
国家自然科学基金重点项目资助 ( 6 99330 10 )
关键词
个性化推荐算法
设计
协同过滤
向量空间
单值分解
相似性
recommendation system, collaborative filtering, vector space, singular value decomposition, similarity