【摘要】：近年來隨著互聯(lián)網(wǎng)技術的高速發(fā)展,學術研究領域也發(fā)生著翻天覆地的變化,網(wǎng)絡上學術論文的數(shù)量呈爆炸式增長。研究人員在網(wǎng)絡上查找其所需要的學術論文信息時,往往需要花費大量的時間和精力,因此如何快速、準確的為研究人員找到其感興趣的學術論文信息成為亟待解決的問題。 本文主要圍繞研究人員學術研究興趣建模,以及如何準確地向研究人員推薦學術論文展開研究。論文在對基于內容推薦算法中的主題模型和協(xié)同過濾方法中的模型推薦方法研究的基礎上,融合兩種推薦方法提出了一種新的混合推薦方法,改善了協(xié)同過濾推薦方法中數(shù)據(jù)稀疏性對于推薦效果的不良影響。本文編碼實現(xiàn)了提出的基于混合模型的學術論文推薦方案,通過實驗確定了方案中的一些參數(shù)取值,并與其他推薦方案進行了對比分析,驗證了本方案的有效性和優(yōu)勢。 本文提出的方案包括一種新的主題模型—-ACTOT(Author Conference Topic Over Time)以及基于該模型的混合推薦模型MFWT (Matrix Factorization With Topic)。ACTOT模型結合了論文的內容信息、發(fā)表期刊／會議信息和發(fā)表時間信息,可以準確地對研究人員的興趣進行建模。MFWT (Matrix Factorization With Topic)模型在實現(xiàn)了基于模型的協(xié)同過濾方法和基于內容的推薦方法的混合,使用ACTOT模型和LDA模型計算的用戶主題向量和論文的主題向量,并分別對PMF(Probabilistic Matrix Factorization)模型中的用戶隱式因子特征向量和論文隱式因子特征向量作正則化處理,修正了PMF模型的推薦結果,有效地改善了評分矩陣稀疏性帶來的不良影響,同時也解決了協(xié)同過濾方法的冷啟動問題。 本文首先分析了學術研究領域現(xiàn)在主流推薦方法的研究現(xiàn)狀和不足之處,然后詳細介紹了本文提出的MFWT混合模型設計方案和實現(xiàn)方法,最后介紹了MFWT模型的實驗驗證和實驗結果分析。
[Abstract]:In recent years, with the rapid development of Internet technology, the academic research field has also undergone earth-shaking changes, the number of academic papers on the network explosive growth. Researchers often spend a lot of time and energy when searching the information of academic papers they need on the network. Therefore, how to find the information of academic papers of interest to researchers quickly and accurately becomes an urgent problem to be solved. This paper focuses on the modeling of researchers' interest in academic research and how to recommend academic papers to researchers accurately. Based on the research of topic model in content-based recommendation algorithm and model recommendation method in collaborative filtering method, a new hybrid recommendation method is proposed. It improves the bad effect of data sparsity on recommendation effect in collaborative filtering recommendation method. In this paper, the proposed scheme of academic thesis recommendation based on hybrid model is implemented, and some parameters of the scheme are determined by experiments, and compared with other schemes, the effectiveness and advantages of this scheme are verified. The scheme proposed in this paper includes a new topic model, ACTOT (Author Conference Topic Over Time), and a hybrid recommendation model based on this model, MFWT (Matrix Factorization With Topic). ACTOT model, which combines the content information of the paper. Publishing journal / conference information and publishing time information can accurately model the. MFWT (Matrix Factorization With Topic) model of researchers' interest in implementing a mixture of model-based collaborative filtering methods and content-based recommendations. Using the user topic vector calculated by ACTOT model and LDA model and the topic vector of the paper, the user implicit factor eigenvector and the paper implicit factor feature vector in PMF (Probabilistic Matrix Factorization) model are regularized, respectively. The recommended results of PMF model are corrected to improve the bad effect of score matrix sparsity and the cold start problem of collaborative filtering method is also solved. This paper first analyzes the research status and shortcomings of the current mainstream recommendation methods in the academic research field, and then introduces the design scheme and implementation method of the MFWT hybrid model proposed in this paper in detail. Finally, the experimental verification of MFWT model and the analysis of experimental results are introduced.
【學位授予單位】：北京郵電大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TP391.3

【參考文獻】

相關期刊論文 前2條

1 王國霞;劉賀平;;個性化推薦系統(tǒng)綜述[J];計算機工程與應用;2012年07期

2 陳樹年;搜索引擎及網(wǎng)絡信息資源的分類組織[J];圖書情報工作;2000年04期

，

本文編號：2303534