本文選題：用戶屬性 + Co-training��； 參考：《工程科學(xué)與技術(shù)》2017年S2期

【摘要】：針對當前基于第三方應(yīng)用數(shù)據(jù)進行用戶屬性預(yù)測算法研究,其較少考慮應(yīng)用前臺實際使用時長問題,由此,本文在應(yīng)用的使用頻率及使用時長的基礎(chǔ)上,構(gòu)造了應(yīng)用前臺均使用時長特征,該特征能進一步刻畫用戶對應(yīng)用的興趣度;同時,為充分利用大量未標注數(shù)據(jù),從多角度特征對用戶屬性進行預(yù)測,由此本文采用了Co-training框架,該框架包含兩個均由棧式自編碼器與神經(jīng)網(wǎng)絡(luò)相結(jié)合的網(wǎng)絡(luò)結(jié)構(gòu)。實驗過程中,對于棧式自編碼算法,先利用未標注的數(shù)據(jù)對網(wǎng)絡(luò)進行參數(shù)初始化,使得網(wǎng)絡(luò)參數(shù)處于一個較優(yōu)的位置,再利用有標注的數(shù)據(jù),采用基于準確率的梯度下降算法,對網(wǎng)絡(luò)參數(shù)進行更新,最終達到收斂。實驗結(jié)果表明,本文算法在準確率、召回率、F1值上均有所提高。
[Abstract]:In view of the current research on user attribute prediction algorithm based on third-party application data, the problem of actual usage time of application foreground is less considered. Therefore, based on the frequency and duration of application, In order to make full use of a large amount of unannotated data and to predict user attributes from multiple angles, the Co-training framework is used in this paper. The framework consists of two networks which are composed of stack self-encoder and neural network. In the process of experiment, for the stack self-coding algorithm, the network parameters are initialized with unlabeled data at first, and the network parameters are placed in a better position. Then, using labeled data, the gradient descent algorithm based on accuracy is adopted. The network parameters are updated and finally converged. The experimental results show that the accuracy and recall rate of the algorithm are improved.
【作者單位】： 四川大學(xué)計算機學(xué)院;
【基金】：國家自然科學(xué)基金資助項目(61332066;81373239)
【分類號】：TP301.6

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本文編號：2002369