基于用户情景的协同过滤推荐

User context based collaborative filtering recommendation

为提高基于项目的协同推荐算法的预测效果,引入用户情景因素。首先计算用户情景因素的相异度矩阵,然后按照用户间相异度大小,采用基于等价相异度矩阵聚类算法对用户进行聚类。在聚类后的用户簇中,选取与目标项目相异度小的项目作为近邻,为用户对目标项目进行评分预测。最后,在标准的MovieLens数据集上进行实验。通过对改进的推荐算法与经典的基于项目的协同推荐算法SlopeOne进行比较,实验数据表明改进后算法的推荐结果有较大提高。

In order to improve the prediction effect of item-based collaborative filtering recommendation algorithm,user context factor was introduced.Firstly the dissimilarity degree matrix of the user context factor was calculated.Then the clustering based on the equivalent dissimilarity degree matrix was adopted to cluster users by dissimilarity value between user and user.After clustering,items that had small dissimilarity value were chosen as neighbors of target item in each user group.These neighbors were used to predict rating of target item for user.Finally,an experiment was given to evaluate the presented approach and to compare it with a typical item-based Slope One algorithm using Movielens dataset.The experimental results suggest that this approach has better performance than Slope One.