【摘要】：針對現(xiàn)有基于矩陣分解的協(xié)同過濾推薦系統(tǒng)預(yù)測精度與推薦精度較低的問題,提出一種改進的矩陣分解方法與協(xié)同過濾推薦系統(tǒng)。首先,將評分矩陣分解為兩個非負矩陣,并對評分做歸一化處理,使其具有概率語義;然后,采用變分推理法計算貝葉斯概率模型實部后驗的分布;最后,搜索相同偏好的用戶分組并預(yù)測用戶的偏好。此外,基于用戶向量的稀疏性設(shè)計一種低計算復(fù)雜度、低存儲成本的推薦結(jié)果決策算法�；�3組公開數(shù)據(jù)集的實驗結(jié)果表明,本算法的預(yù)測性能以及推薦系統(tǒng)的效果均優(yōu)于其他預(yù)測算法與推薦算法。
[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.
【作者單位】： 塔里木大學(xué)信息工程學(xué)院;
【基金】：國家科技支撐計劃(2013BAH27F00) 塔里木大學(xué)校長基金項目(TDZKQN201616) 新疆南疆農(nóng)業(yè)信息化研究中心項目(TSAI201402)資助
【分類號】：TP391.3

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