本文選題：用戶興趣學(xué)習(xí)　切入點：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。一方面，信息化給人們帶來了極大的便利；另一方面，由于過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越困難。傳統(tǒng)搜索是基于關(guān)鍵詞檢索的，但這種方法無法有效提取和檢索到語義間的關(guān)聯(lián)內(nèi)容和隱含信息，在知識發(fā)現(xiàn)和查準(zhǔn)查全率方面都有所欠缺。而個性化Web搜索技術(shù)的出現(xiàn)，可以有效緩解上述問題的出現(xiàn)，為用戶提供更精細、準(zhǔn)確和自動化的搜索。 本文研究基于興趣學(xué)習(xí)的Web內(nèi)容推薦系統(tǒng)并對其進行優(yōu)化，根據(jù)用戶搜索所涉及的領(lǐng)域本體添加用戶興趣領(lǐng)域至用戶本體，，通過概念和語義間的關(guān)系計算用戶興趣權(quán)重，并根據(jù)用戶瀏覽行為實時更新本體，得到更準(zhǔn)確的用戶興趣模型。由于用戶興趣作為搜索限制條件加入搜索語句，無疑增加了系統(tǒng)響應(yīng)時間，本文通過研究圖論算法，對搜索條件進行重新排序，通過選擇估值減少中間結(jié)果集，選擇高效的執(zhí)行計劃，提高連接查詢效率，從而減少搜索響應(yīng)時間，給用戶創(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，研究用戶興趣學(xué)習(xí)算法，以達到提高用戶查詢搜索準(zhǔn)確度的目的。由于用戶興趣增加了查詢條件的復(fù)雜性，又通過查詢優(yōu)化策略優(yōu)化查詢時間，以達到提高用戶查詢搜索效率的目的。并對查詢優(yōu)化策略進行實驗和其他方法的搜索引擎進行對比，驗證了該方法可有效提高查詢效率。通過研究及優(yōu)化，改進后的基于興趣學(xué)習(xí)的Web內(nèi)容推薦系統(tǒng)在為用戶推薦信息上將更符合用戶的興趣，同時查詢效率也將有所提升。 通過實驗，將搜索結(jié)果按照用戶興趣模型重新排序后返回給用戶，用戶的滿意度有所提高，可以看出改進后的用戶興趣模型更接近用戶真實興趣，可以減少翻頁和搜索時間，給用戶更愉悅的用戶體驗。將用戶興趣作為限制條件加入查詢語句后的搜索系統(tǒng)，查詢時間將會有所增加，經(jīng)過本文方法的查詢優(yōu)化，在查詢效率上也比優(yōu)化前有所提高，尤其針對查詢條件和語句關(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)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：TP391.3

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