融合评分上下文和物品相似度的推荐算法

Recommendation algorithm with rating context and item similarity

推荐系统中用户的评分往往会受到评分上下文的影响,即用户先前对一些物品的评分会影响其对当前物品评分的客观性。稀疏线性方法在计算物品相似度时将受到上下文影响的用户评分与其他评分同等看待,然而该部分评分并不能客观地反映出物品之间的相似度。针对以上问题,在稀疏线性方法的基础上提出了融合评分上下文和物品相似度的推荐算法,算法分为三个阶段:第一个阶段使用加权评分计算物品最近邻进行特征选择;第二个阶段利用评分误差权重减少算法模型对受到上下文影响的评分的拟合,训练得出物品相似度矩阵;第三个阶段根据用户评分和物品相似度进行评分预测以完成物品推荐。在MovieLens的四个数据集上进行实验,采用平均准确率(MAP)、平均倒数排名(MRR)和归一化折损累计增益(NDCG)指标来评估算法效果。实验结果表明,融合评分上下文将进一步提高物品相似度的准确性,从而提高推荐的性能。

In the recommendation system,the user’s ratings are often affected by the rating context,that is,the user’s previous ratings of some items will affect the objectivity of his rating of the current item.Sparse linear method treats user ratings affected by context as the same as other ratings when calculating item similarity.However,this partial ratings cannot objectively reflect the similarity between items.To solve the above problems,this paper proposed a recommendation algorithm combining rating context and item similarity based on sparse linear method.It divided the algorithm into three stages.The first stage used weighted ratings to calculate the item’s nearest neighbor for feature selection.In the second stage,it used the rating error weight to reduce the fitting of the ratings affected by the context of the algorithm model,and trained the item similarity matrix.In the third stage,it predicted the ratings according to the user’s ratings and the item similarity,and finally sorted the predicted ratings to complete the item recommendation.Experiments were conducted on four datasets of MovieLens,it used mean average precision(MAP),mean reciprocal rank(MRR)and normalized discounted cumulative gain(NDCG)to evaluate the effectiveness of the algorithm.The experimental results show that the fusion rating context will further improve the accuracy of item similarity and thus improve the performance of recommendation.