本文選題：電子商務(wù) + 大數(shù)據(jù)　； 參考：《科技通報(bào)》2017年09期

【摘要】：傳統(tǒng)的概率矩陣分解技術(shù),忽視了信息消費(fèi)者間的信任關(guān)系和關(guān)注關(guān)系,使其推薦性能和推薦質(zhì)量不斷下降,針對(duì)以上問(wèn)題,提出基于多元正態(tài)概率模型的貝葉斯概率矩陣分解算法,以多元正態(tài)概率模型作為先驗(yàn)分布,實(shí)驗(yàn)中通過(guò)計(jì)算Gibbs sampling過(guò)程中迭代次數(shù)來(lái)達(dá)到數(shù)據(jù)的稀疏性。在聯(lián)合概率未知和條件概率易得等情況下,引用Gibbs sampling技術(shù)進(jìn)行計(jì)算,實(shí)驗(yàn)中引用MAE、RMSE兩種方法進(jìn)行誤差評(píng)價(jià)。實(shí)驗(yàn)結(jié)果表明:在稀疏矩陣的檢測(cè)中,改進(jìn)的貝葉斯概率矩陣分解算法的預(yù)測(cè)精密度更加穩(wěn)定,對(duì)緩解矩陣稀疏性問(wèn)題更加有效。
[Abstract]:The traditional probability matrix decomposition technology neglects the relationship of trust and concern among information consumers, which causes the performance and quality of recommendation to decline. A Bayesian probability matrix decomposition algorithm based on multivariate normal probability model is proposed. The multivariate normal probability model is used as the prior distribution and the data sparsity is achieved by calculating the iterations in the Gibbs sampling process. Under the condition that the joint probability is unknown and the conditional probability is easy to obtain, the Gibbs sampling technique is used to calculate the error, and the mae RMSE method is used to evaluate the error in the experiment. The experimental results show that the prediction precision of the improved Bayesian probability matrix decomposition algorithm is more stable in the detection of sparse matrix, and it is more effective to alleviate the sparse problem of matrix.
【作者單位】： 湖南大學(xué)新聞傳播與攝影藝術(shù)學(xué)院;開(kāi)封大學(xué)實(shí)驗(yàn)實(shí)訓(xùn)中心;
【分類(lèi)號(hào)】：O212.8

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本文編號(hào)：1959934