本文選題：社會化搜索 + 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)下流行的社會網(wǎng)絡(luò)等等。搜索引擎解決了人們在互聯(lián)網(wǎng)海量信息當(dāng)中快速便捷地獲取有效內(nèi)容的問題，社會網(wǎng)絡(luò)在虛擬網(wǎng)絡(luò)世界建立了類現(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)搜索引擎對任何用戶的相同搜索請求都會返回相同搜索結(jié)果，在進(jìn)行個(gè)性化服務(wù)轉(zhuǎn)型過程，搜索引擎往往只是根據(jù)用戶興趣等因素對用戶單獨(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，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)。搜索引擎與社會網(wǎng)絡(luò)的融合，催生了社會化搜索的相關(guān)研究。 然而，社會化搜索的研究還處于一個(gè)起步階段，研究都對于社會化搜索如何將搜索引擎與社會網(wǎng)絡(luò)結(jié)合起來都有不同的認(rèn)識。本文從社會網(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)同對社會化搜索排序進(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.采用社會網(wǎng)絡(luò)分析法對搜索引擎進(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詞和網(wǎng)頁的鏈接關(guān)系，通過基于不確定圖的SimRank算法，計(jì)算查詢詞與網(wǎng)頁的相似度，最終以相似度和查詢詞的加權(quán)方式建立網(wǎng)頁描述庫。 2.從分析用戶搜索經(jīng)驗(yàn)入手，計(jì)算社會網(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)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：TP391.3

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