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中文摘要：
协同过滤算法作为一种成功的个性化推荐技术已经被应用到很多领域,但是由于协同过滤算法所使用的用户-项目评分矩阵一般都非常稀疏,导致该算法推荐效果一直较差。文章在考虑了用户相似喜好、项目平均得分的差异性和方差等因素基础上,提出了一种项目间综合相似度计算方法JAV Weighted Model,通过在MovieLens数据集上的实验表明本文方法在预测精度上与已有方法相比有一定的提高。
英文摘要：
Collaborative filtering is a successfully-used algorithm for personalized recommendation,which is widely applied to many fields,but the user-item rating matrix used in collaborative filtering is generally very sparse and leads to poor result.Based on the consideration of user＇s taste similarity,the difference of average score of different items and the variance of different items,an integrated item similarity calculation method-JAV Weighted Model was introduced.The new model is validates through experiments on MovieLens dataset.The result shows that the prediction accuracy of this method has improved reasonably compared with other existing methods.
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