【摘要】 协同过滤是到目前为止最成功和应用最广泛的推荐技术,然而,由于用户-项目矩阵极端稀疏导致推荐不精确。针对该问题,提出了三种数据填充方法和两种推荐策略。对评分矩阵中未评分数据的三种数据填充方法是:(1)采用行和列数据的加权平均值填充;(2)采用行和列数据的众数的平均值填充;(3)采用行和列数据的中位数的平均值填充。一种推荐策略是直接用填充数据作为预测评分进行推荐;另一种推荐策略是将填充数据后的评分矩阵作为伪评分矩阵,应用Pearson相关相似性进行协同过滤推荐。采用MovieLens数据集进行的实验结果表明:上述几种推荐策略均可有效地缓解评分数据稀疏性问题,且提高了推荐精确度。更多还原

【Abstract】 Till now , collaborative filtering has been the most successful and widely used technology in recommender systems.However , the rating data is extremely sparse so as to affect the prediction accuracy seriously in traditional collaborative filtering.In order to overcome the drawback , in this paper , we proposed three data filling approaches and two recommendation strategies.These data filling approaches for non-rating data in rating matrix are :( 1 ) Filling data using weighed average of row and column data ;( 2 ) Filling data using mode average of row and column data ; and ( 3 ) Filling data using median average of row and column data.One of the recommendation strategies is taking filling data for predicative rating directly , and another is to set the rating matrix filled data as a pseudo rating matrix and collaborative filtering applying Pearson correlation.The experimental results on the MovieLens data set show that all these recommendation strategies can effectively alleviate the trouble of rating data sparseness and gain better recommendation accuracy.更多还原