本文選題：用戶(hù)興趣學(xué)習(xí)　切入點(diǎn)：Web內(nèi)容推薦　出處：《華中科技大學(xué)》2012年碩士論文

【摘要】：隨著Internet的飛速發(fā)展，互聯(lián)網(wǎng)已成為全球最大的分布式信息數(shù)據(jù)庫(kù)。一方面，信息化給人們帶來(lái)了極大的便利；另一方面，由于過(guò)量冗余的信息充斥網(wǎng)絡(luò)，想要在網(wǎng)絡(luò)上快速有效的提取有效信息也變得越來(lái)越困難。傳統(tǒng)搜索是基于關(guān)鍵詞檢索的，但這種方法無(wú)法有效提取和檢索到語(yǔ)義間的關(guān)聯(lián)內(nèi)容和隱含信息，在知識(shí)發(fā)現(xiàn)和查準(zhǔn)查全率方面都有所欠缺。而個(gè)性化Web搜索技術(shù)的出現(xiàn)，可以有效緩解上述問(wèn)題的出現(xiàn)，為用戶(hù)提供更精細(xì)、準(zhǔn)確和自動(dòng)化的搜索。 本文研究基于興趣學(xué)習(xí)的Web內(nèi)容推薦系統(tǒng)并對(duì)其進(jìn)行優(yōu)化，根據(jù)用戶(hù)搜索所涉及的領(lǐng)域本體添加用戶(hù)興趣領(lǐng)域至用戶(hù)本體，，通過(guò)概念和語(yǔ)義間的關(guān)系計(jì)算用戶(hù)興趣權(quán)重，并根據(jù)用戶(hù)瀏覽行為實(shí)時(shí)更新本體，得到更準(zhǔn)確的用戶(hù)興趣模型。由于用戶(hù)興趣作為搜索限制條件加入搜索語(yǔ)句，無(wú)疑增加了系統(tǒng)響應(yīng)時(shí)間，本文通過(guò)研究圖論算法，對(duì)搜索條件進(jìn)行重新排序，通過(guò)選擇估值減少中間結(jié)果集，選擇高效的執(zhí)行計(jì)劃，提高連接查詢(xún)效率，從而減少搜索響應(yīng)時(shí)間，給用戶(hù)創(chuàng)造更準(zhǔn)確快捷的結(jié)果返回。 本文首先介紹基于興趣學(xué)習(xí)的Web內(nèi)容推薦涉及的核心技術(shù)，在此基礎(chǔ)上，研究用戶(hù)興趣學(xué)習(xí)算法，以達(dá)到提高用戶(hù)查詢(xún)搜索準(zhǔn)確度的目的。由于用戶(hù)興趣增加了查詢(xún)條件的復(fù)雜性，又通過(guò)查詢(xún)優(yōu)化策略?xún)?yōu)化查詢(xún)時(shí)間，以達(dá)到提高用戶(hù)查詢(xún)搜索效率的目的。并對(duì)查詢(xún)優(yōu)化策略進(jìn)行實(shí)驗(yàn)和其他方法的搜索引擎進(jìn)行對(duì)比，驗(yàn)證了該方法可有效提高查詢(xún)效率。通過(guò)研究及優(yōu)化，改進(jìn)后的基于興趣學(xué)習(xí)的Web內(nèi)容推薦系統(tǒng)在為用戶(hù)推薦信息上將更符合用戶(hù)的興趣，同時(shí)查詢(xún)效率也將有所提升。 通過(guò)實(shí)驗(yàn)，將搜索結(jié)果按照用戶(hù)興趣模型重新排序后返回給用戶(hù)，用戶(hù)的滿意度有所提高，可以看出改進(jìn)后的用戶(hù)興趣模型更接近用戶(hù)真實(shí)興趣，可以減少翻頁(yè)和搜索時(shí)間，給用戶(hù)更愉悅的用戶(hù)體驗(yàn)。將用戶(hù)興趣作為限制條件加入查詢(xún)語(yǔ)句后的搜索系統(tǒng)，查詢(xún)時(shí)間將會(huì)有所增加，經(jīng)過(guò)本文方法的查詢(xún)優(yōu)化，在查詢(xún)效率上也比優(yōu)化前有所提高，尤其針對(duì)查詢(xún)條件和語(yǔ)句關(guān)系較為復(fù)雜的情況，優(yōu)化效果更為顯著。
[Abstract]:With the rapid development of Internet, the Internet has become the largest distributed information database in the world. It is also becoming more and more difficult to extract effective information quickly and effectively on the network. Traditional search is based on keyword retrieval, but this method can not effectively extract and retrieve the associated content and hidden information between semantics. The emergence of personalized Web search technology can effectively alleviate the above problems and provide users with more precise accurate and automated search. This paper studies and optimizes the Web content recommendation system based on interest learning, adds the domain of interest to user ontology according to the domain ontology involved in user search, and calculates the weight of user interest through the relationship between concepts and semantics. According to the user browsing behavior, the ontology is updated in real time, and a more accurate user interest model is obtained. Because user interest is added to the search sentence as a search constraint, the response time of the system is undoubtedly increased, and the graph theory algorithm is studied in this paper. The search conditions are reordered to reduce the intermediate result set by selecting the estimation and the efficient execution plan to improve the efficiency of the join query so as to reduce the search response time and create a more accurate and fast result return for the user. This paper first introduces the core technology of Web content recommendation based on interest learning, and then studies the algorithm of user interest learning. In order to improve the search accuracy of users, because of the complexity of the query conditions, the query time is optimized by the query optimization strategy, because the interest of the user increases the complexity of the query conditions. In order to improve the search efficiency of users, the experiment of query optimization strategy is compared with the search engine of other methods, and it is proved that this method can effectively improve the efficiency of query. The improved Web content recommendation system based on interest learning will be more in line with the user's interest in recommending information, and the query efficiency will also be improved. Through experiments, the search results are reordered according to the user interest model and returned to the user. The user satisfaction is improved. It can be seen that the improved user interest model is closer to the real user interest and can reduce page turning and searching time. To give users a more pleasant user experience. After adding user interest as a restriction condition to the query system, the query time will be increased, and the query efficiency will also be improved after the optimization of the method in this paper. Especially in the case of complex query condition and statement relationship, the optimization effect is more remarkable.
【學(xué)位授予單位】：華中科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類(lèi)號(hào)】：TP391.3

【參考文獻(xiàn)】

相關(guān)期刊論文 前1條

1 汪錦嶺,金蓓弘,李京;一種高效的RDF圖模式匹配算法[J];計(jì)算機(jī)研究與發(fā)展;2005年10期

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