基于合作模式的學者影響力預測研究
發(fā)布時間：2019-01-20 08:38
【摘要】：隨著學術(shù)社會的不斷發(fā)展,近年來學術(shù)論文的產(chǎn)量不斷增長,學術(shù)信息不斷豐富,學術(shù)大數(shù)據(jù)逐漸成為一個新興的研究領域。學術(shù)影響力評估是學術(shù)社會中必不可少的一部分。在學術(shù)社會中,我們可以通過選擇跟蹤有影響力的學者,獲得某一領域的最新研究進展;在學校職稱評估中,我們可以通過學者的學術(shù)影響力來決定職稱的評定;在基金申請中,往往具有高影響力的學者可以更有可能獲得資助。然而,隨著大數(shù)據(jù)時代的來臨,學術(shù)信息過載問題逐漸成為學術(shù)合作的壁壘。即,學者們很難權(quán)衡一個學者的影響力。因此,如何能夠有效的獲取和預測一個學者的影響力是一個亟待解決的問題。為了解決學術(shù)大數(shù)所帶來的問題和挑戰(zhàn),研究者們從不同角度對各種不同學術(shù)關(guān)系進行研究和分析。在本文中,我們主要研究學術(shù)大數(shù)據(jù)中,學者的影響力預測問題,即給定一個學者,預測其在若干年后的學術(shù)影響力。這有助于了解學者的研究能力和公正的評價一個學者的學術(shù)能力,從而為基金分配,職稱評定等等實際問題提供幫助,解決學術(shù)信息過載問題。同時,深度學習,是近些年機器學習研究中的一個領軍方向,深度學習的核心思想是,通過分析和模擬人腦在處理數(shù)據(jù)時的方式方法,通過模仿人類神經(jīng)網(wǎng)絡的思考方式來處理數(shù)據(jù)。在數(shù)據(jù)量龐大的信息化時代,深度學習在預測領域也可發(fā)揮其巨大的作用。同時,區(qū)別于以往的基于論文引用的學術(shù)影響力預測方法,我們提出基于合作模式的學術(shù)影響力預測方法。本文不同于傳統(tǒng)的學術(shù)影響力預測技術(shù),提出一種基于深度學習的學術(shù)影響力預測方法。通過對深度學習方法的學習和深入研究,將其引入到傳統(tǒng)的預測算法中。在基于學者合作模式的學者影響力預測技術(shù)中,我們采用了自動編碼器(Stacked Autoencoder),提出了基于合作模式的學術(shù)影響力預測模型SIP(Scholar Impact Prediction),包括基于學術(shù)合作網(wǎng)絡的特征提取,和基于編碼器的影響力預測兩個部分。首先,基于學術(shù)合作網(wǎng)絡,我們提取學者的個人屬性和網(wǎng)絡屬性等學者合作模式作為深度學習訓練的輸入特征。然后根據(jù)得到的屬性特征集,利用自動編碼器算法學習模型參數(shù)來進行學術(shù)影響力預測。本文在DBLP學術(shù)數(shù)據(jù)對所提出的SIP算法進行評估,結(jié)果表明,與傳統(tǒng)的機器學習算法對比,我們提出的基于合作模式的學術(shù)影響力預測模型在平均絕對誤差,均方根誤差和皮爾遜相關(guān)系數(shù)等指標上表現(xiàn)較好,表明了深度學習在學術(shù)影響力預測的性能。同時,我們分析了各個特征屬性對預測結(jié)果的影響,可以幫我們更加深刻的理解不同學者合作模式對預測結(jié)果的影響。另一方面,本文探索的學術(shù)社會中的影響力問題,在現(xiàn)實生活中,在不同的在線社交網(wǎng)絡中,也存在同樣的問題,由于網(wǎng)絡屬性的不同,預測技術(shù)各有不同,同時彼此相通。因此,我們所提出的預測方法,具有一定的普適性,也具備應用到其他預測或者推薦系統(tǒng)之中。例如,電子商務中的物品推薦,在線社交媒體中的還有推薦,以及基于興趣的物品推薦等等。因此,我們所提出的預測策略具有一定的普適性,對其他預測技術(shù)有一定的借鑒意義。
[Abstract]:With the development of the academic society, in recent years, the output of the academic papers has been growing, the academic information is constantly enriched, and the large-scale academic data has gradually become a new field of research. The evaluation of academic influence is an essential part of the academic society. In the academic society, we can obtain up-to-date research progress in a certain field through the selection of influential scholars; in the assessment of the title of the school, we can determine the assessment of the professional title through the academic influence of the scholars; in the application of the fund, Scholars who tend to have a high impact can be more likely to be funded. However, with the advent of large data age, the problem of academic information overload has become a barrier to academic cooperation. That is, it is hard for scholars to weigh the influence of a scholar. Therefore, how to effectively acquire and predict the influence of a scholar is an urgent problem to be solved. In order to solve the problems and challenges brought by the large number of academic studies, the researchers have studied and analyzed various academic relations from different angles. In this paper, we mainly study the influence prediction of the scholars in the academic big data, that is, given a scholar, the academic influence of the scholar after several years is predicted. This will help to understand the research ability of the scholars and the impartial evaluation of the academic ability of a scholar, so as to provide help to the practical problems such as fund distribution, job evaluation and so on, and solve the problem of the overload of the academic information. At the same time, the depth study is a leading direction in the study of machine learning in recent years. The core idea of depth study is to process the data by analyzing and simulating the way the human brain processes the data. In the era of large data volume, depth study can also play a great role in the field of prediction. At the same time, it is different from the previous academic influence prediction method based on the paper reference, and we put forward the method of predicting the academic influence based on the cooperative model. This paper is different from the traditional academic influence prediction technology, and puts forward a method of academic influence prediction based on depth study. The learning and in-depth study of the depth learning method is introduced into the traditional prediction algorithm. In the field of the scholars' influence prediction based on the cooperative model of the scholars, we have adopted the Stamped Autoencer, and put forward the model of academic influence prediction (SIP) based on the cooperation model, including the feature extraction based on the academic cooperation network. and predicting two parts based on the influence of the encoder. First, on the basis of the academic cooperation network, we extract the personal attributes and the network attributes of the scholars as the input feature of the depth study training. and then using the automatic encoder algorithm to study the model parameters to predict the academic influence according to the obtained attribute set. In this paper, the proposed SIP algorithm is evaluated by the DBLP academic data. The results show that, compared with the traditional machine learning algorithm, the model of the academic influence based on the cooperative model is better than the average absolute error, the root mean square error and the Pearson correlation coefficient. The performance of depth learning in the prediction of academic influence is shown. At the same time, we analyze the effect of each feature attribute on the prediction result, and can help us to understand the effect of different scholars' cooperation model on the prediction result. On the other hand, the influence of this paper in the academic society, in the real life, in different online social networks, there are also the same problems, because of the different network attributes, the prediction techniques are different, and at the same time they are in communication with each other. Therefore, the prediction method proposed by us has a certain universality and is also applied to other prediction or recommendation systems. e. g., an item recommendation in e-commerce, a further recommendation in an online social media, and an interest-based item recommendation, and the like. Therefore, the prediction strategy proposed by us has a certain universality, which can be used for reference for other prediction techniques.
【學位授予單位】：吉林大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP181
本文編號：2411860
[Abstract]:With the development of the academic society, in recent years, the output of the academic papers has been growing, the academic information is constantly enriched, and the large-scale academic data has gradually become a new field of research. The evaluation of academic influence is an essential part of the academic society. In the academic society, we can obtain up-to-date research progress in a certain field through the selection of influential scholars; in the assessment of the title of the school, we can determine the assessment of the professional title through the academic influence of the scholars; in the application of the fund, Scholars who tend to have a high impact can be more likely to be funded. However, with the advent of large data age, the problem of academic information overload has become a barrier to academic cooperation. That is, it is hard for scholars to weigh the influence of a scholar. Therefore, how to effectively acquire and predict the influence of a scholar is an urgent problem to be solved. In order to solve the problems and challenges brought by the large number of academic studies, the researchers have studied and analyzed various academic relations from different angles. In this paper, we mainly study the influence prediction of the scholars in the academic big data, that is, given a scholar, the academic influence of the scholar after several years is predicted. This will help to understand the research ability of the scholars and the impartial evaluation of the academic ability of a scholar, so as to provide help to the practical problems such as fund distribution, job evaluation and so on, and solve the problem of the overload of the academic information. At the same time, the depth study is a leading direction in the study of machine learning in recent years. The core idea of depth study is to process the data by analyzing and simulating the way the human brain processes the data. In the era of large data volume, depth study can also play a great role in the field of prediction. At the same time, it is different from the previous academic influence prediction method based on the paper reference, and we put forward the method of predicting the academic influence based on the cooperative model. This paper is different from the traditional academic influence prediction technology, and puts forward a method of academic influence prediction based on depth study. The learning and in-depth study of the depth learning method is introduced into the traditional prediction algorithm. In the field of the scholars' influence prediction based on the cooperative model of the scholars, we have adopted the Stamped Autoencer, and put forward the model of academic influence prediction (SIP) based on the cooperation model, including the feature extraction based on the academic cooperation network. and predicting two parts based on the influence of the encoder. First, on the basis of the academic cooperation network, we extract the personal attributes and the network attributes of the scholars as the input feature of the depth study training. and then using the automatic encoder algorithm to study the model parameters to predict the academic influence according to the obtained attribute set. In this paper, the proposed SIP algorithm is evaluated by the DBLP academic data. The results show that, compared with the traditional machine learning algorithm, the model of the academic influence based on the cooperative model is better than the average absolute error, the root mean square error and the Pearson correlation coefficient. The performance of depth learning in the prediction of academic influence is shown. At the same time, we analyze the effect of each feature attribute on the prediction result, and can help us to understand the effect of different scholars' cooperation model on the prediction result. On the other hand, the influence of this paper in the academic society, in the real life, in different online social networks, there are also the same problems, because of the different network attributes, the prediction techniques are different, and at the same time they are in communication with each other. Therefore, the prediction method proposed by us has a certain universality and is also applied to other prediction or recommendation systems. e. g., an item recommendation in e-commerce, a further recommendation in an online social media, and an interest-based item recommendation, and the like. Therefore, the prediction strategy proposed by us has a certain universality, which can be used for reference for other prediction techniques.
【學位授予單位】：吉林大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP181
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