基于混合模型的學(xué)術(shù)論文推薦方法研究
發(fā)布時(shí)間：2018-10-31 21:00
【摘要】：近年來(lái)隨著互聯(lián)網(wǎng)技術(shù)的高速發(fā)展,學(xué)術(shù)研究領(lǐng)域也發(fā)生著翻天覆地的變化,網(wǎng)絡(luò)上學(xué)術(shù)論文的數(shù)量呈爆炸式增長(zhǎng)。研究人員在網(wǎng)絡(luò)上查找其所需要的學(xué)術(shù)論文信息時(shí),往往需要花費(fèi)大量的時(shí)間和精力,因此如何快速、準(zhǔn)確的為研究人員找到其感興趣的學(xué)術(shù)論文信息成為亟待解決的問(wèn)題。 本文主要圍繞研究人員學(xué)術(shù)研究興趣建模,以及如何準(zhǔn)確地向研究人員推薦學(xué)術(shù)論文展開(kāi)研究。論文在對(duì)基于內(nèi)容推薦算法中的主題模型和協(xié)同過(guò)濾方法中的模型推薦方法研究的基礎(chǔ)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(guò)濾推薦方法中數(shù)據(jù)稀疏性對(duì)于推薦效果的不良影響。本文編碼實(shí)現(xiàn)了提出的基于混合模型的學(xué)術(shù)論文推薦方案,通過(guò)實(shí)驗(yàn)確定了方案中的一些參數(shù)取值,并與其他推薦方案進(jìn)行了對(duì)比分析,驗(yàn)證了本方案的有效性和優(yōu)勢(shì)。 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(jié)合了論文的內(nèi)容信息、發(fā)表期刊／會(huì)議信息和發(fā)表時(shí)間信息,可以準(zhǔn)確地對(duì)研究人員的興趣進(jìn)行建模。MFWT (Matrix Factorization With Topic)模型在實(shí)現(xiàn)了基于模型的協(xié)同過(guò)濾方法和基于內(nèi)容的推薦方法的混合,使用ACTOT模型和LDA模型計(jì)算的用戶主題向量和論文的主題向量,并分別對(duì)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(jié)果,有效地改善了評(píng)分矩陣稀疏性帶來(lái)的不良影響,同時(shí)也解決了協(xié)同過(guò)濾方法的冷啟動(dòng)問(wèn)題。 本文首先分析了學(xué)術(shù)研究領(lǐng)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(xì)介紹了本文提出的MFWT混合模型設(shè)計(jì)方案和實(shí)現(xiàn)方法,最后介紹了MFWT模型的實(shí)驗(yàn)驗(yàn)證和實(shí)驗(yàn)結(jié)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(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TP391.3
本文編號(hào)：2303534
[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(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TP391.3
【參考文獻(xiàn)】
相關(guān)期刊論文 前2條
1 王國(guó)霞;劉賀平;;個(gè)性化推薦系統(tǒng)綜述[J];計(jì)算機(jī)工程與應(yīng)用;2012年07期
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，本文編號(hào)：2303534
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