本文選題：Web搜索 + 鏈接分析�。� 參考：《中南大學(xué)》2012年博士論文

【摘要】：隨著信息技術(shù)的飛速發(fā)展,互聯(lián)網(wǎng)信息量正呈爆炸性增長,萬維網(wǎng)己成為一個巨大而復(fù)雜的信息空間,人們己從信息缺乏轉(zhuǎn)變?yōu)樾畔⑦^載。互聯(lián)網(wǎng)信息具有分散、無序、海量等特點,如何從浩瀚的信息資源中快速、有效、準(zhǔn)確地找到所需信息是一個具有挑戰(zhàn)性的研究課題,Web搜索正成為互聯(lián)網(wǎng)領(lǐng)域的研究熱點和焦點之一。傳統(tǒng)的Web搜索算法注重于Web的鏈接結(jié)構(gòu)和Web頁面等級權(quán)重,而忽略了用戶的興趣行為,導(dǎo)致了部分搜索結(jié)果不完整及準(zhǔn)確率低。此外,通過迭代計算出每個網(wǎng)頁的Hub值和Authority值的方式,導(dǎo)致Web搜索的效率較低,并容易出現(xiàn)一定的分散和泛化現(xiàn)象。針對傳統(tǒng)的Web搜索算法存在的缺點,本文在總結(jié)和分析國內(nèi)外相關(guān)研究工作的基礎(chǔ)上,充分結(jié)合用戶的興趣行為和相關(guān)的智能優(yōu)化算法來展開研究,主要研究內(nèi)容及創(chuàng)新性工作概括如下： (1)綜述了有關(guān)搜索引擎結(jié)構(gòu)及其工作流程、傳統(tǒng)Web搜索算法設(shè)計思路和啟發(fā)式算法模型的研究成果及方法,為研究Web搜索算法基礎(chǔ)理論的研究者提供參考和借鑒。 (2)在分析現(xiàn)有用戶興趣模型表示方式的基礎(chǔ)上,針對Web搜索的特點,結(jié)合用戶瀏覽行為、用戶反饋行為、關(guān)鍵詞權(quán)重以及短期興趣和長期興趣等相關(guān)因素,設(shè)計了一種基于Web搜索的用戶興趣模型,為后續(xù)研究Web環(huán)境下的啟發(fā)式搜索算法奠定基礎(chǔ)。 (3)在充分結(jié)合遺傳量子算法和克隆選擇算法優(yōu)點的基礎(chǔ)上提出一種克隆遺傳量子搜索算法(Clonal Genetic Quantum Search Algorithm, CGQSA),詳細(xì)介紹了該算法的設(shè)計思路和框架,并運用Markov鏈理論對其收斂性進行分析。同時,具體分析了該算法的計算復(fù)雜度,實驗結(jié)果表明CGQSA算法具備良好的穩(wěn)定性和可擴展性,其性能明顯優(yōu)于其它的傳統(tǒng)Web搜索算法和啟發(fā)式算法。 (4)結(jié)合關(guān)鍵詞的鏈接權(quán)重和Web頁的鏈接結(jié)構(gòu),設(shè)計一種評估Web頁平均權(quán)重的數(shù)學(xué)模型,將每個Web頁表示成種群中的一個個體,并用一個適應(yīng)度函數(shù)對其性能進行評估。 (5)在遺傳算法的基礎(chǔ)上,融入模擬退火算法的思想,提出一種遺傳模擬退火搜索算法(Genetic Simulated Annealing SearchAlgorithm, GSASA),詳細(xì)介紹了該算法的設(shè)計思路和框架,并對其收斂性進行了具體分析。GSASA算法將遺傳算法和模擬退火算法的優(yōu)點充分結(jié)合起來,并充分考慮Web搜索的實際應(yīng)用環(huán)境,在較大程度上提高了算法的運行效率和求解質(zhì)量。仿真實驗取得了較理想的實驗結(jié)果,從而表明該方法是可行和有效的。 我們所得結(jié)果是Web搜索算法理論方面的一些一般性的理論成果,這些成果對于設(shè)計與實現(xiàn)Web搜索算法仍然具有指導(dǎo)意義。更重要的是,我們所引入的分析手段與方法對于Web搜索算法的相關(guān)理論研究具有較為廣泛的適用性和參考價值。
[Abstract]:With the rapid development of information technology, the amount of information on the Internet is increasing explosively. The World wide Web has become a huge and complex information space, people have changed from lack of information to information overload. Internet information has the characteristics of dispersion, disorder, magnanimity and so on. How to get from the vast information resources quickly and effectively, It is a challenging research topic to find the needed information accurately. Web search is becoming one of the research hotspots and focal points in the field of Internet. The traditional Web search algorithm focuses on the link structure of the Web and the weight of the Web page, but neglects the user's interest behavior, which leads to partial incomplete search results and low accuracy. In addition, by iterating out the Hub value and Authority value of each web page, the efficiency of web search is low, and the phenomenon of dispersion and generalization is easy to appear. In view of the shortcomings of the traditional Web search algorithm, based on the summary and analysis of the related research work at home and abroad, this paper fully combines the user's interest behavior and the related intelligent optimization algorithm to carry out the research. The main research contents and innovative work are summarized as follows: (1) the research results and methods of search engine structure and its workflow, traditional Web search algorithm design ideas and heuristic algorithm model are summarized. This paper provides a reference for the researchers who study the basic theory of Web search algorithm. (2) based on the analysis of the existing user interest model, according to the characteristics of Web search, combined with user browsing behavior, user feedback behavior. A Web search based user interest model is designed based on key words weight, short-term interest, long-term interest and other related factors. Based on the advantages of genetic quantum algorithm and clone selection algorithm, a clone genetic quantum search algorithm is proposed. CGQSAA, the design idea and framework of the algorithm are introduced in detail. The convergence is analyzed by Markov chain theory. At the same time, the computational complexity of the algorithm is analyzed in detail. The experimental results show that the CGQSA algorithm has good stability and scalability. Its performance is obviously superior to other traditional Web search algorithms and heuristic algorithms. (4) combining the link weight of keywords and the link structure of Web pages, a mathematical model is designed to evaluate the average weight of Web pages. Each Web page is represented as an individual in the population, and its performance is evaluated by a fitness function. A genetic simulated Annealing search algorithm (GSASAA) is proposed. The design idea and framework of the algorithm are introduced in detail, and the convergence of the algorithm is analyzed in detail, which combines the advantages of genetic algorithm and simulated annealing algorithm. Considering the practical application environment of Web search, the running efficiency and solving quality of the algorithm are improved to a large extent. The simulation results show that the method is feasible and effective. Our results are some general theoretical achievements in the theory of Web search algorithms, which are still of guiding significance for the design and implementation of Web search algorithms. More importantly, the analytical means and methods we introduced have a wide range of applicability and reference value for the theoretical research of Web search algorithms.
【學(xué)位授予單位】：中南大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2012
【分類號】：TP391.3

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本文編號：2027466