本文選題：搜索引擎 + 社交搜索��； 參考：《杭州電子科技大學(xué)》2017年碩士論文

【摘要】：目前,網(wǎng)民獲取信息的一個(gè)重要方式就是在線搜索。但是隨著WEB 2.0和各類(lèi)社交網(wǎng)站的日益崛起,傳統(tǒng)搜索引擎的重大缺陷日益凸顯。即各種Web2.0網(wǎng)站和社交網(wǎng)站擁有極為豐富的用戶數(shù)據(jù),但傳統(tǒng)搜索引擎卻無(wú)法支持對(duì)其檢索。對(duì)用戶而言,不同的用戶因?yàn)槁殬I(yè)、愛(ài)好、學(xué)歷及社交關(guān)系有所不同,對(duì)搜索結(jié)果的期待也就有所不同,因此對(duì)獲取個(gè)性化的搜索結(jié)果有著迫切的需求。對(duì)企業(yè)而言,企業(yè)迫切需要用戶參與的新型搜索方式的普及,從而能夠獲取更多用戶信息,以便更好地發(fā)展客戶關(guān)系和提供個(gè)性化服務(wù)。鑒于此,本文提出一個(gè)新的搜索系統(tǒng),命名為PERSO,意圖使用PERSO系統(tǒng)實(shí)現(xiàn)基于用戶社交行為的個(gè)性化搜索目標(biāo)。該系統(tǒng)通過(guò)爬取開(kāi)放的在線社交網(wǎng)絡(luò)數(shù)據(jù),分析豐富的用戶特征及社交關(guān)系,把用戶最感興趣的結(jié)果反饋在最靠前的位置,從而改善了傳統(tǒng)搜索引擎的搜索結(jié)果。用戶建模是個(gè)性化社交搜索的關(guān)鍵,本文根據(jù)國(guó)內(nèi)最大最開(kāi)放的社交網(wǎng)站新浪微博的數(shù)據(jù)特征,提出了多層次多維度的用戶模型,包括一級(jí)(用戶自身社交行為)、二級(jí)(朋友社交行為)、三級(jí)(社交擴(kuò)張)社交相關(guān)度模型,后者是前者的補(bǔ)充,全面地描述了社交網(wǎng)絡(luò)中的用戶特征。在用戶建模的基礎(chǔ)上,本文提出將社交相關(guān)度模型集成到web文本搜索處理過(guò)程的三種方法,即三種網(wǎng)頁(yè)排序機(jī)制:先文本特征過(guò)濾后社交特征排序的兩步驟TP排序;先社交特征過(guò)濾后文本特征排序的兩步驟PT排序;社交與文本共排序的一步驟HB排序。最后,本文以1000萬(wàn)篇百度百科文檔和20位真實(shí)的新浪微博用戶的社交數(shù)據(jù)為數(shù)據(jù)來(lái)源,以F1和nDCG@K為評(píng)價(jià)指標(biāo),設(shè)計(jì)執(zhí)行了四組實(shí)驗(yàn):三種層級(jí)的社交化相關(guān)度模型的測(cè)評(píng)實(shí)驗(yàn);三種排序機(jī)制的測(cè)評(píng)實(shí)驗(yàn);用戶與好友信息對(duì)搜索效果的影響力對(duì)比測(cè)評(píng)實(shí)驗(yàn);好友數(shù)量對(duì)搜索效果的影響力測(cè)評(píng)實(shí)驗(yàn)。實(shí)驗(yàn)證明了本文排序機(jī)制的有效性,以及每一級(jí)模型對(duì)搜索結(jié)果不同的改善程度。
[Abstract]:At present, an important way for Internet users to obtain information is online search. However, with the rise of Web 2.0 and various social networking sites, traditional search engines become more and more flawed. Web 2.0 sites and social networking sites are rich in user data, but traditional search engines cannot retrieve them. For users, different users have different expectations for search results because of their different occupations, hobbies, academic qualifications and social relationships, so there is an urgent need to obtain personalized search results. For enterprises, enterprises urgently need the popularization of new search methods in which users participate, so as to obtain more user information, in order to better develop customer relations and provide personalized services. In view of this, this paper proposes a new search system named Perso, which is intended to realize the personalized search target based on user's social behavior. By crawling open online social network data and analyzing rich user characteristics and social relationships, the system feedback the most interesting results to the front, thus improving the search results of traditional search engines. User modeling is the key to personalized social search. According to the data features of Sina Weibo, the largest and most open social network in China, this paper puts forward a multi-level and multi-dimensional user model. It includes one level (user's own social behavior), two (friend's social behavior), three (social expansion) social relevance model, the latter is the supplement of the former, which comprehensively describes the characteristics of users in the social network. On the basis of user modeling, this paper proposes three methods to integrate the social correlation model into the web text search process, that is, three kinds of web page sorting mechanisms: first text feature filtering and then two step TP sorting of social features; Social feature filtering and text feature sorting are two steps PT sorting and one step HB sorting of social and text cosorting. Finally, taking 10 million Baidu encyclopedia documents and 20 real Sina Weibo users' social data as data source, using F1 and nDCGGK as evaluation indexes, four groups of experiments are designed and implemented: three levels of social correlation model; Three kinds of ranking mechanism evaluation experiment; user and friend information on the impact of the search effect comparison test; the number of friends on the impact of the search effect evaluation experiment. The experimental results show that the ranking mechanism is effective and the search results are improved by different models at each level.
【學(xué)位授予單位】：杭州電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TP393.09;TP391.3

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