本文選題：用戶瀏覽行為 + 用戶興趣�。� 參考：《東北大學》2013年碩士論文

【摘要】：隨著Web上資源的急劇膨脹,面對用戶提供的有限查詢詞,當前的搜索引擎提供的千人一面的搜索已經難以滿足用戶對搜索結果的需求。在用戶使用搜索引擎進行信息檢索的過程中,依據用戶的實際興趣為用戶返回個性化的搜索結果可以提高用戶對搜索結果的滿意度。大量研究表明用戶的實際興趣與其在網頁上的瀏覽行為是密切相關的,通過用戶瀏覽行為分析可以獲取用戶興趣信息,進而構建用戶興趣模型,使搜索結果更加貼近用戶的期望。然而,目前的隱式用戶興趣獲取方法無法很好的預測出用戶對頁面的實際興趣度。究其原因,一方面是由于當前研究尚未考慮到用戶的瀏覽行為可能隨搜索任務類型的不同而變化。另一方面,當前的用戶興趣獲取方法多使用某種特定用戶行為預測用戶興趣度。 針對上述問題,本文探究用戶瀏覽行為在不同類型的搜索任務中所表現出的差異,并研究聯合分析多種用戶瀏覽行為的隱式用戶興趣獲取方法。在此基礎上構建適當用戶興趣模型,最終得出用戶的實際興趣,從而實現個性化服務,使搜索結果更加貼近用戶的期望。 具體的,本文將任務類型分為導航型、信息型、事務型三種不同類型,將用戶的基本瀏覽行為轉換為頁面停留時間時間、鼠標點擊次數、頁面重訪問次數以及滑塊移動次數四種可分析行為事件。通過Bernard提出的算法完成了任務類型的自動識別,分析了四種可分析行為事件在不同搜索任務類型中表現出的差異。在用戶行為分析階段,本文基于M5模型樹對可分析事件建模完成對用戶興趣度的計算,在計算過程中樹的剪枝和相關系數平滑是建模過程中必須考慮的問題。模型評價階段,本文使用模型準確率評價指標將不區(qū)分任務類型和區(qū)分任務類型的模型與Nicholas Belkin的模型進行了對比。為了清晰有效的表達用戶興趣信息,本文提出了基于分類的用戶興趣模型,該模型涉及對文檔的特征值提取,基于搜狗語料的SVM分類器對相關文檔進行分類等技術。使用準確率和排序準確率兩個指標將baidu搜索引擎和基于VSM的模型及基于分類的模型進行了對比。實驗結果表明,本文提出的面向用戶興趣的用戶行為分析模型可有效提高用戶對搜索結果的滿意度。
[Abstract]:With the rapid expansion of resources on Web, facing the limited query words provided by users, the current search engine can not meet the demand of search results. In the process of information retrieval by using search engine, the user's satisfaction with search results can be improved by returning personalized search results to users according to their actual interests. A large number of studies show that the actual interest of users is closely related to their browsing behavior on the web. Through the analysis of user browsing behavior, user interest information can be obtained and then user interest models can be constructed. Make search results closer to user expectations. However, the current implicit user interest acquisition method can not predict the actual user interest in the page. On the one hand, the current research has not considered that the browsing behavior of users may vary with the type of search task. On the other hand, the current user interest acquisition methods often use a specific user behavior to predict user interest. To solve the above problems, this paper explores the differences of user browsing behavior in different types of search tasks, and studies an implicit user interest acquisition method which can jointly analyze multiple user browsing behaviors. On this basis, the appropriate user interest model is constructed, and finally the actual interest of the user is obtained, thus the personalized service is realized, and the search results are closer to the user's expectation. Specifically, the task type is divided into three types: navigation type, information type and transaction type. The basic browsing behavior of the user is converted into page stay time, mouse click times, etc. Page revisits and slider moves four analyzable behavior events. The automatic recognition of task types is accomplished by the algorithm proposed by Bernard, and the differences of four analyzable behavior events in different search task types are analyzed. In the phase of user behavior analysis, the user interest is calculated based on M5 model tree. In the process of computing, the tree pruning and correlation coefficient smoothing are the problems that must be considered in the modeling process. In the stage of model evaluation, the model that does not distinguish between task type and task type is compared with that of Nicholas Belkin. In order to express user interest information clearly and effectively, this paper proposes a classification-based user interest model, which involves the extraction of feature values of documents and the classification of relevant documents by SVM classifier based on Sogou corpus. The baidu search engine is compared with the model based on VSM and the model based on classification by using accuracy and sorting accuracy. The experimental results show that the user behavior analysis model proposed in this paper can effectively improve the users' satisfaction with the search results.
【學位授予單位】：東北大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：TP391.3

【參考文獻】

中國期刊全文數據庫 前1條

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本文編號：1814889