计算机科学 ›› 2010, Vol. 37 ›› Issue (5): 181-183.
• 人工智能 • 上一篇 下一篇
田伟,许静,彭玉青
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TIAN Wei,XU Jing,PENG Yu-qing
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摘要: 协同过滤(CF)个性化推荐算法过程中,用户相似度计算是CF技术的核心问题之一。以用户显式、离散评分的条件为基础,应用概率论分析方法,考察了用户显式离散评分向量,提出了改进方法:全面均值法结合对商品进行分类推荐的改进算法。分析表明这种方案更适合推荐系统实际应用环境。实际数据实验表明,新方法提高了CF推荐的预测精度和推荐质量。
关键词: 电子商务,推荐系统,协同过滤,全面均值,个性化
Abstract: The users’similarity computation is a key step of Collaborative Filter (CF) algorithm. The All-Average method and classified recommendation improved algorithm based on probabilistic analysis of users' discrete explicit rating vector were proposed to solve the problem of CF sparsity and other practical problems. Experimental result shows the improved method enhances the precision and quality of CF prediction.
Key words: Electronic commerce, Recommendation system, Collaborative filter, All-average, Personalized
田伟,许静,彭玉青. 基于离散评分向量概率分析的CF算法改进研究[J]. 计算机科学, 2010, 37(5): 181-183. https://doi.org/
TIAN Wei,XU Jing,PENG Yu-qing. Research on CF Algorithm Based on Probabilistic Analysis of Discrete Rating Vector[J]. Computer Science, 2010, 37(5): 181-183. https://doi.org/
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