【摘要】 针对推荐算法的信息过期问题,结合遗忘函数和信息保持期的改进时间权重引入矩阵分解模型,提出一种基于改进时间权重的矩阵分解协同过滤算法(MFTWCF,MF-based and improved time weighted collabora tive filtering),相比前人提出的基于改进时间权重的邻域协同过滤算法(NTWCF,neighborhood-based and improved time weighted collaboratire filering algorithm),准确性显著提升了26.58%。由于过去的信息所包含的特征在随后的时间里可能被用户持续关注,从而增强过期信息对推荐的影响力,所以提出了融合时间权重和类型影响力加强权重的改进算法(MFTTWCF,MF-bosed and imporved time and type weighteel collaborative filtering)修正上述时间权重。电影数据集的实验证明,MFTTWCF算法预测的准确性比MFTWCF算法提高了3.58%,能够取得更好的推荐效果,适用于通过预测评分进行推荐的系统。更多还原

【Abstract】 In order to solve the problem of information expiration of the recommender systems,we introduced the improved time weight of forgetting function and information retention period into matrix factorization model(MF)and proposed a MF-based and improved-time weighted collaborative filtering algorithm(MFTWCF)whose prediction accuracy had been raised by about 26.58% compared with that of neighborhood-based and improved-time weighted collaborative filtering algorithm(NTWCF).In view of the facts that users could continuously get access to some characteristics of past information,which would have greater influence for recommendation,we proposed the type weight to strengthen the information influence and to correct the improved time weight in MFTWCF.The new improved algorithm is called MF-based improved-time and type weighted collaborative filtering algorithm(MFTTWCF).The results of movie dataset experiments show that the prediction accuracy of MFTTWCF algorithm is 3.58% higher than that of MFTWCF algorithm and can achieve better recommendation effect.And it is applicable to recommender systems with rating prediction.更多还原