为了提高个性化推荐系统的准确率,提出了一种基于内容的加权粒度序列推荐算法.通过分析项目属性关系将项目粒度化,计算每个粒度的贡献度得到项目特征矩阵.再根据用户行为信息生成用户粒度序列并进行粒度映射,利用Apriori算法提取出用户偏好矩阵.最后将项目特征矩阵和用户偏好矩阵做乘积运算,其结果代入改进的sigmoid函数中进行喜好概率预测,从而完成Top-N项目推荐.实验选取MovieLens数据集,结果表明基于内容的加权粒度序列的推荐算法准确率达到72.27％,高于当前流行的推荐算法;在效率方面,推荐时间少于相同用户数量下的协同过滤推荐算法;综合测度评分为0.393,充分验证了算法的整体性能优于其他推荐算法.%In order to improve the accuracy of personalized recommendation system,a content-based weighted granularity sequence recommendation algorithm is proposed.The item is turned to be granularities by analyzing the relationship between the item properties.The item characteristic matrix is obtained by calculating the contribution degree of each granularity.Then,according to the information of user behaviors,the user granularity sequence is generated and the granularity mapping is performed.The user preference matrix is extracted by using Apriori algorithm.Finally,the item characteristic matrix is multiplied by the user preference matrix,and the result is introduced into the improved sigmoid function to predict the preference probability,thus completing the Top-N item recommendation.Experiments on MovieLens dataset show that the content-based of weighted granularity sequence recommendation algorithm achieves an accuracy rate of 72.27％,which is higher than the accuracy rate of the current popular recommendation algorithms.In terms of efficiency,its recommended time is less than that of the collaborative filtering recommendation algorithm with the same number of users.The F1-score is 0.393,fully verifying that the overall performance of the proposal is better than that of other recommendation algorithms.
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