本文選題：社會(huì)化搜索 + SimRank�。� 參考：《哈爾濱工程大學(xué)》2013年碩士論文

【摘要】：互聯(lián)網(wǎng)經(jīng)過(guò)幾十年的發(fā)展，已經(jīng)極大程度上融入到了人們的現(xiàn)實(shí)生活當(dāng)中，隨著產(chǎn)業(yè)與需求的發(fā)展，互聯(lián)網(wǎng)被劃分為幾大入口，包括搜索引擎、瀏覽器、即時(shí)通信以及當(dāng)下流行的社會(huì)網(wǎng)絡(luò)等等。搜索引擎解決了人們?cè)诨ヂ?lián)網(wǎng)海量信息當(dāng)中快速便捷地獲取有效內(nèi)容的問(wèn)題，社會(huì)網(wǎng)絡(luò)在虛擬網(wǎng)絡(luò)世界建立了類(lèi)現(xiàn)實(shí)的人際關(guān)系網(wǎng)絡(luò)，拉近了人與人之間的距離。 搜索引擎與社會(huì)網(wǎng)絡(luò)作為兩大互聯(lián)網(wǎng)入口，不能孤立發(fā)展。傳統(tǒng)搜索引擎對(duì)任何用戶的相同搜索請(qǐng)求都會(huì)返回相同搜索結(jié)果，在進(jìn)行個(gè)性化服務(wù)轉(zhuǎn)型過(guò)程，搜索引擎往往只是根據(jù)用戶興趣等因素對(duì)用戶單獨(dú)的個(gè)性化服務(wù)，用戶彼此的個(gè)性化信息不能夠被相互借鑒。社會(huì)網(wǎng)絡(luò)為用戶相互借鑒個(gè)性化信息提供了良好的基礎(chǔ)平臺(tái)，用戶在進(jìn)行搜索時(shí)不再是孤軍奮戰(zhàn)，，而協(xié)同好友共同完成一次搜索任務(wù)。搜索引擎與社會(huì)網(wǎng)絡(luò)的融合，催生了社會(huì)化搜索的相關(guān)研究。 然而，社會(huì)化搜索的研究還處于一個(gè)起步階段，研究都對(duì)于社會(huì)化搜索如何將搜索引擎與社會(huì)網(wǎng)絡(luò)結(jié)合起來(lái)都有不同的認(rèn)識(shí)。本文從社會(huì)網(wǎng)絡(luò)可為搜索引擎提供協(xié)同式服務(wù)的角度出發(fā)，基于隱式協(xié)同對(duì)社會(huì)化搜索排序進(jìn)行深入研究。 本文的主要研究工作包括以下幾個(gè)方面： 1.采用社會(huì)網(wǎng)絡(luò)分析法對(duì)搜索引擎進(jìn)行日志分析，以不確定圖的方式邏輯表示搜索引擎的日志中查詢(xún)?cè)~和網(wǎng)頁(yè)的鏈接關(guān)系，通過(guò)基于不確定圖的SimRank算法，計(jì)算查詢(xún)?cè)~與網(wǎng)頁(yè)的相似度，最終以相似度和查詢(xún)?cè)~的加權(quán)方式建立網(wǎng)頁(yè)描述庫(kù)。 2.從分析用戶搜索經(jīng)驗(yàn)入手，計(jì)算社會(huì)網(wǎng)絡(luò)中用戶的信任度。在建立用戶間信任度量的基礎(chǔ)上提出隱式協(xié)同模型。 3.結(jié)合前兩方面工作，綜合提出社會(huì)化搜索排序算法。
[Abstract]:After decades of development, the Internet has been greatly integrated into people's real life. With the development of industry and demand, the Internet has been divided into several portals, including search engines, browsers, Instant messaging and the current popularity of social networks and so on. Search engine solves the problem that people can get effective content quickly and conveniently in the mass information of Internet. Social network has set up a kind of realistic interpersonal network in the virtual network world, which brings people closer to each other. Search engine and social network as two big Internet entrance, cannot develop in isolation. The traditional search engine will return the same search result to any user with the same search request. In the process of personalized service transformation, the search engine usually only individualizes the user according to the user's interest and other factors. Users' personalized information cannot be used for reference. Social network provides a good basic platform for users to learn from personalized information, users are no longer alone in searching, and cooperate with friends to complete a search task. The fusion of search engine and social network has given birth to the relevant research of social search. However, the research of social search is still in its infancy, which has different understanding on how to combine social search engine with social network. From the point of view that social network can provide collaborative service for search engine, this paper makes a deep research on social search ranking based on implicit collaboration. The main research work of this paper includes the following aspects: 1. The social network analysis method is used to analyze the search engine log. The query words in the search engine log and the link relationship between the web page and the query word in the search engine log are logically represented by the uncertain graph, and the SimRank algorithm based on the uncertain graph is adopted. The similarity between query words and web pages is calculated. Finally, the web page description library is established by similarity and weight of query words. 2. Based on the analysis of user search experience, the trust degree of users in social network is calculated. Based on the establishment of trust between users, an implicit cooperative model is proposed. 3. Combined with the first two aspects of work, a comprehensive social search sorting algorithm is proposed.
【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(lèi)號(hào)】：TP391.3

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 張應(yīng)龍;李翠平;陳紅;杜凌霞;;不確定圖上的kNN查詢(xún)處理[J];計(jì)算機(jī)研究與發(fā)展;2011年10期

2 張春陽(yáng);周繼恩;錢(qián)權(quán);蔡慶生;;抽樣在數(shù)據(jù)挖掘中的應(yīng)用研究[J];計(jì)算機(jī)科學(xué);2004年02期

3 賴(lài)相旭;韓立新;曾曉勤;王敏;吳勝利;;基于信息量與信息熵的元搜索引擎排序算法研究[J];計(jì)算機(jī)科學(xué);2012年03期

4 劉凱鵬;方濱興;;一種基于社會(huì)性標(biāo)注的網(wǎng)頁(yè)排序算法[J];計(jì)算機(jī)學(xué)報(bào);2010年06期

5 袁野;王國(guó)仁;;面向不確定圖的概率可達(dá)查詢(xún)[J];計(jì)算機(jī)學(xué)報(bào);2010年08期

6 喬秀全;楊春;李曉峰;陳俊亮;;社交網(wǎng)絡(luò)服務(wù)中一種基于用戶上下文的信任度計(jì)算方法[J];計(jì)算機(jī)學(xué)報(bào);2011年12期

7 李亞楠;許晟;王斌;;基于加權(quán)SimRank的中文查詢(xún)推薦研究[J];中文信息學(xué)報(bào);2010年03期

8 宋巍;張宇;劉挺;李生;;基于檢索歷史上下文的個(gè)性化查詢(xún)重構(gòu)技術(shù)研究[J];中文信息學(xué)報(bào);2010年03期

9 靳延安;李瑞軒;文坤梅;辜希武;盧正鼎;段東圣;;社會(huì)標(biāo)注及其在信息檢索中的應(yīng)用研究綜述[J];中文信息學(xué)報(bào);2010年04期

10 馬云龍;林原;林鴻飛;;基于權(quán)重標(biāo)準(zhǔn)化SimRank方法的查詢(xún)擴(kuò)展技術(shù)研究[J];中文信息學(xué)報(bào);2011年01期

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