基于改进贝叶斯概率模型的推荐算法
发布时间：2018-08-29 19:49
【摘要】：针对现有基于矩阵分解的协同过滤推荐系统预测精度与推荐精度较低的问题,提出一种改进的矩阵分解方法与协同过滤推荐系统。首先,将评分矩阵分解为两个非负矩阵,并对评分做归一化处理,使其具有概率语义;然后,采用变分推理法计算贝叶斯概率模型实部后验的分布;最后,搜索相同偏好的用户分组并预测用户的偏好。此外,基于用户向量的稀疏性设计一种低计算复杂度、低存储成本的推荐结果决策算法。基于3组公开数据集的实验结果表明,本算法的预测性能以及推荐系统的效果均优于其他预测算法与推荐算法。
[Abstract]:An improved matrix decomposition method and collaborative filtering recommendation system are proposed to solve the problem of low prediction accuracy and recommendation accuracy of collaborative filtering recommendation system based on matrix decomposition. First, the scoring matrix is decomposed into two non-negative matrices, and the score is normalized to make it have probabilistic semantics. Then, the variational reasoning method is used to calculate the posterior distribution of the real part of Bayesian probabilistic model. Search for groups of users with the same preferences and predict preferences. In addition, a recommendation result decision algorithm with low computational complexity and low storage cost is designed based on user vector sparsity. The experimental results based on three groups of open data sets show that the prediction performance of this algorithm and the effect of recommendation system are superior to those of other prediction algorithms and recommendation algorithms.
【作者单位】： 塔里木大学信息工程学院;
【基金】：国家科技支撑计划(2013BAH27F00) 塔里木大学校长基金项目(TDZKQN201616) 新疆南疆农业信息化研究中心项目(TSAI201402)资助
【分类号】：TP391.3
本文编号：2212235
[Abstract]:An improved matrix decomposition method and collaborative filtering recommendation system are proposed to solve the problem of low prediction accuracy and recommendation accuracy of collaborative filtering recommendation system based on matrix decomposition. First, the scoring matrix is decomposed into two non-negative matrices, and the score is normalized to make it have probabilistic semantics. Then, the variational reasoning method is used to calculate the posterior distribution of the real part of Bayesian probabilistic model. Search for groups of users with the same preferences and predict preferences. In addition, a recommendation result decision algorithm with low computational complexity and low storage cost is designed based on user vector sparsity. The experimental results based on three groups of open data sets show that the prediction performance of this algorithm and the effect of recommendation system are superior to those of other prediction algorithms and recommendation algorithms.
【作者单位】： 塔里木大学信息工程学院;
【基金】：国家科技支撑计划(2013BAH27F00) 塔里木大学校长基金项目(TDZKQN201616) 新疆南疆农业信息化研究中心项目(TSAI201402)资助
【分类号】：TP391.3
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