【摘要】： 本文主要討論網(wǎng)頁排序相關(guān)算法,重點討論了鏈接分析技術(shù)。 首先,介紹了網(wǎng)頁排序的基本原理,對幾種較為常用的網(wǎng)頁排序技術(shù)進(jìn)行了對比分析;著重剖析了兩種典型的鏈接分析算法:PageRank和HITS,分析了它們各自的優(yōu)劣。 PageRank算法主要缺陷是將PageRank值在所有的出鏈接上進(jìn)行平均分配,沒有很好地考慮語義信息,很容易受到無關(guān)鏈接的影響,產(chǎn)生主題漂移。本文設(shè)計了一個簡單的計算模型改進(jìn)PageRank算法,該計算模型在PageRank算法平均分配的基礎(chǔ)之上,考慮了鏈接相似度信息,并利用樸素貝葉斯模型對鏈接相似度信息進(jìn)行評估。由于考慮了出鏈接與目標(biāo)網(wǎng)頁相似度信息,使得那些沒有價值的頁面(廣告頁面)被分得較少的PageRank值,提升了真正有價值的頁面所分得的PageRank值。 最后,本文應(yīng)用上述模型實現(xiàn)了一個模擬的搜索引擎。該模擬系統(tǒng)包含了搜索引擎的幾乎全部功能,并在互聯(lián)網(wǎng)真實環(huán)境下請一些用戶進(jìn)行實際測試,對上述算法進(jìn)行驗證。小范圍用戶測試結(jié)果表明:融入了鏈接相似度信息之后,提升了搜索結(jié)果的用戶滿意度。
[Abstract]:In this paper, we mainly discuss the related algorithms of web page sorting, and focus on the link analysis technology. First of all, this paper introduces the basic principle of web page sorting, compares and analyzes several common web page sorting techniques, and emphatically analyzes two typical link analysis algorithms: PageRank and HITS, which analyze their advantages and disadvantages respectively. The main drawback of PageRank algorithm is that the PageRank value is distributed evenly on all out links, and the semantic information is not considered very well, so it is easy to be influenced by irrelevant links, resulting in topic drift. In this paper, a simple computing model is designed to improve the PageRank algorithm. Based on the average allocation of the PageRank algorithm, the link similarity information is considered, and the naive Bayesian model is used to evaluate the link similarity information. Considering the similarity information between links and target pages, those pages (advertising pages) with no value are divided into fewer PageRank values, which improves the PageRank value of truly valuable pages. Finally, a simulated search engine is implemented by using the above-mentioned model. The simulation system contains almost all the functions of the search engine, and some users are asked to test the algorithm in the real environment of the Internet to verify the algorithm mentioned above. The results of a small-scale user test show that the user satisfaction of the search results can be improved by incorporating the link similarity information.
【學(xué)位授予單位】：南京理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2008
【分類號】：TP391.3

【引證文獻(xiàn)】

相關(guān)碩士學(xué)位論文 前2條

1 吳世勇;基于聚類分析的搜索引擎自動性能評價研究[D];江西師范大學(xué);2010年

2 李宜兵;基于搜索引擎網(wǎng)頁排序算法研究[D];沈陽理工大學(xué);2011年

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本文編號：2432886