本文選題：論文推薦 + 聚類�。� 參考：《內蒙古大學》2017年碩士論文

【摘要】：隨著學術論文數(shù)量呈爆炸式增長,科研工作者如何從龐大的論文庫中迅速找到感興趣的文獻成為亟待解決的難題。學術論文推薦是克服此難題的有效方法。學術論文推薦研究主要集中于基于內容過濾、基于引文網絡、基于合著網絡和基于論文評價指標等方法。基于內容過濾的論文推薦是指根據(jù)用戶的歷史操作、評論、興趣標注等信息建立用戶模型并推薦。然而,這種推薦方法需要在信息的收集上花費大量時間�；谝木W絡的論文推薦是利用論文之間的引用關系來向用戶推薦論文,但是引用關系本身具有的不確定性常常會影響推薦結果的質量�；诤现W絡的論文推薦是一種利用學者間通過合著而形成的復雜網絡進行推薦的方法�；谡撐脑u價指標的論文推薦通過論文或作者的引用、共引、期刊質量因子和H指數(shù)等評價指標對論文過濾并推薦。本文在已有研究的基礎上,提出一種基于多興趣的學術論文推薦算法,主要貢獻如下:(1)識別學者的多個研究興趣。根據(jù)學者通常具有多個研究興趣的事實,利用聚類算法將每位學者的發(fā)表論文集劃分為多個興趣集,每個興趣集都代表學者的一個研究興趣。(2)分別提出基于VSM和基于頻繁模式的兩種多興趣學者模型�；赩SM的多興趣學者模型將一個興趣集中所有發(fā)表論文的模型加權融合,并將融合后的特征向量作為相應的興趣模型�；陬l繁模式的多興趣學者模型首先使用LDA預處理興趣集,然后使用FP-Growth算法從處理結果中挖掘一個頻繁模式集,最后化簡該頻繁模式集并建立相應的興趣模型。(3)提出研究興趣重視度的概念,并根據(jù)興趣集中論文的數(shù)目給出研究興趣重視度的計算公式,同時將其引入到兩種多興趣學者模型中。我們利用真實的數(shù)據(jù)進行了三組對比實驗。結果表明,與已有的算法相比,基于多興趣的學術論文推薦算法提高了推薦準確率。
[Abstract]:With the explosive growth of the number of academic papers, how to quickly find the interested documents from the huge database of papers has become a difficult problem to be solved. The recommendation of academic papers is an effective way to overcome this problem. The research on the recommendation of academic papers is mainly focused on the methods of content filtering, citation network, co-authoring network and evaluation index. Content filtering based paper recommendation refers to the establishment of user model and recommendation based on user's historical operation, comment, interest tagging and other information. However, this method of recommendation takes a lot of time to collect information. The paper recommendation based on citation network makes use of the citation relation between papers to recommend the paper to the user, but the uncertainty of the citation relationship itself often affects the quality of the recommendation result. The paper recommendation based on coauthor network is a method of making use of the complex network formed by scholars. The paper recommendation based on the paper evaluation index is filtered and recommended by the paper or the author's citation, co-citation, periodical quality factor and H index. In this paper, based on the existing research, a multi-interest recommendation algorithm for academic papers is proposed. The main contributions of this algorithm are as follows: 1) recognition of multiple research interests of scholars. According to the fact that scholars usually have more than one research interest, each published paper collection is divided into multiple interest sets by clustering algorithm. Each interest set represents a research interest of a scholar. (2) two kinds of multi-interest scholar models based on VSM and frequent pattern are proposed respectively. The multi-interest scholar model based on VSM is a weighted fusion of all the published models in a set of interests, and the fused feature vector is taken as the corresponding interest model. The multi-interest scholar model based on frequent pattern first uses LDA to preprocess interest set, and then uses FP-Growth algorithm to mine a frequent pattern set from the processing result. Finally, the frequent pattern set is simplified and the corresponding interest model is established. (3) the concept of the interest degree of interest is proposed, and the calculation formula of the interest degree of interest is given according to the number of papers in the interest set. At the same time, it is introduced into two kinds of multi-interest scholars' models. We conducted three sets of comparative experiments using real data. The results show that, compared with the existing algorithms, the recommendation algorithm based on multi-interest academic papers improves the accuracy of recommendation.
【學位授予單位】：內蒙古大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.3

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本文編號：1941957