【摘要】：科技文獻(xiàn)的高速增長(zhǎng)使得科研信息的檢索難度大大增加，雖然搜索引擎在很大程度上減輕了科研人員檢索論文的工作，但它缺少對(duì)科研人員個(gè)性化需求的考量，難以在搜索結(jié)果中進(jìn)一步找到與其興趣相關(guān)內(nèi)容，而推薦系統(tǒng)能夠有效解決這些問題。 本文首先介紹了常見推薦算法及其優(yōu)缺點(diǎn)，然后介紹了合著網(wǎng)絡(luò)分析方法和技術(shù)，研究了社會(huì)學(xué)中的科研合作現(xiàn)象，分析了網(wǎng)絡(luò)整體特征和節(jié)點(diǎn)重要性，驗(yàn)證了合著網(wǎng)絡(luò)的復(fù)雜網(wǎng)絡(luò)特性，解釋了學(xué)者的社會(huì)性和團(tuán)體性。單一推薦算法由于自身缺陷和應(yīng)用限制，在論文推薦效果上并不理想。根據(jù)研究現(xiàn)狀及不足，本文從以下幾方面研究混合推薦算法的設(shè)計(jì)： 1.為準(zhǔn)確描述用戶興趣，，用已發(fā)表論文相關(guān)信息構(gòu)建動(dòng)態(tài)用戶興趣模型，同時(shí)用論文質(zhì)量評(píng)價(jià)方法描述論文重要性，在二者的基礎(chǔ)上提出一種混合推薦算法； 2.為減少推薦的盲目性，將社會(huì)學(xué)中的合著網(wǎng)絡(luò)引入混合推薦算法中，定義了不同用戶之間合作強(qiáng)度計(jì)算方式，對(duì)合著網(wǎng)絡(luò)進(jìn)行社團(tuán)劃分以限制合作強(qiáng)度傳播范圍； 3.用戶對(duì)排名靠前的論文具有高閱讀傾向，為衡量混合推薦算法對(duì)結(jié)果的排序能力，引入信息檢索系統(tǒng)評(píng)價(jià)指標(biāo)—平均準(zhǔn)確率和平均排序倒數(shù)對(duì)Top-N推薦效果進(jìn)行評(píng)價(jià)。 實(shí)驗(yàn)結(jié)果表明混合推薦算法相對(duì)于單一推薦算法具有較優(yōu)的推薦效果，引入社團(tuán)劃分的混合推薦算法具有更優(yōu)的推薦效果。
[Abstract]:The rapid growth of scientific and technological literature has greatly increased the difficulty of searching scientific research information. Although the search engine has greatly alleviated the work of searching papers for scientific researchers, it lacks the consideration of the individual needs of scientific researchers. It is difficult to find the content related to its interest in the search results, and the recommendation system can effectively solve these problems. This paper first introduces the common recommendation algorithms and their advantages and disadvantages, then introduces the co-author network analysis methods and techniques, studies the phenomenon of scientific research cooperation in sociology, analyzes the overall characteristics of the network and the importance of nodes. The complex network characteristics of co-authored networks are verified, and the sociality and collectivity of scholars are explained. Single recommendation algorithm is not ideal because of its own defects and application limitations. According to the present situation and deficiency of the research, this paper studies the design of hybrid recommendation algorithm from the following aspects: 1. In order to accurately describe user interest, a dynamic user interest model is constructed with relevant information of published papers. At the same time, the paper quality evaluation method is used to describe the importance of the paper, on the basis of which a hybrid recommendation algorithm is proposed. In order to reduce the blindness of recommendation, the co-author network in sociology is introduced into the hybrid recommendation algorithm, and the calculation method of cooperation intensity between different users is defined, and the co-author network is divided into communities to limit the spread range of cooperation intensity; 3. Users have a high reading tendency to the top papers. In order to measure the ability of hybrid recommendation algorithm to sort the results, the evaluation index of information retrieval system-average accuracy and the reciprocal of average ranking are introduced to evaluate the effect of Top-N recommendation. The experimental results show that the hybrid recommendation algorithm has a better recommendation effect than a single recommendation algorithm, and the hybrid recommendation algorithm based on community partition has a better recommendation effect.
【學(xué)位授予單位】：哈爾濱工程大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：TP391.3

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