本文關鍵詞： 個性化推薦 協同過濾 用戶屬性 興趣對比度　出處：《華中師范大學》2014年碩士論文　論文類型：學位論文

【摘要】：為解決信息過載問題和應對用戶對個性化服務的需求,個性化推薦技術應運而生,本文希望通過對個性化推薦的優(yōu)化與創(chuàng)新,讓用戶能夠更快更精準的找到自己想要的資源。在眾多個性化推薦技術中,協同過濾算法是當下研究的熱門。因為其算法的應用范圍最廣泛,發(fā)展時間最長,算法最成熟。協同過濾推薦主要是根據存貯在系統(tǒng)數據庫中用戶歷史消費及評分數據,來分析用戶的興趣,預測用戶未來可能消費什么樣的產品,從而對其實施個性化推薦。以往的協同過濾算法研究,主要是以用戶評分矩陣為基礎,進行用戶偏好的感知,以用戶打分的相似性來判斷用戶之間興趣的相似性。隨著算法的發(fā)展,特別是可擴展性問題、冷啟動問題等算法瓶頸的出現,純粹依賴評分矩陣數據來尋找最近鄰,就顯得力不從心。因此,必須尋找其他有效的用戶偏好數據來源。本文對用戶偏好的感知方法進行改進,引入了用戶屬性信息這一重要的偏好感知數據源,與評分矩陣共同構成用戶偏好感知的數據基礎。用戶屬性作為描述用戶個體特征的重要信息,不同的屬性可以將用戶劃分到不同類別的群體當中,這些用戶群可能存在一定的興趣偏好相似性,將這些特定用戶群的共同興趣找出來,作為產生推薦的基礎。本文定義了一個新的衡量用戶興趣偏好的參數,即：興趣對比度。在此基礎上,提出了一個融合用戶屬性和興趣對比度的協同過濾個性化推薦算法,該算法以用戶屬性組合為約束,結合興趣對比度共同產生待推薦集合,經過整理刪選后形成最后的推薦列表。本文提出的新算法,將克服傳統(tǒng)協同過濾的可擴展性問題作為改進的目標。在新算法的整個流程設計中,不依賴傳統(tǒng)的用戶相似性計算來尋找最近鄰。因此,當用戶和項目快速增長時,不會出現算法復雜度急劇上升的情況,實驗證明,融合用戶屬性和興趣對比度的協同過濾推薦算法,能夠在保證推薦實時性的前提下,達到滿意的推薦質量,是一種靈活高效的推薦方案,更重要是提供了一種新的推薦思路。此外,本文還對不同屬性組合下的推薦效率進行了系統(tǒng)分析,為該領域的相關研究奠定了一定的基礎。
[Abstract]:In order to solve the problem of information overload and to meet the needs of users for personalized service, personalized recommendation technology emerges as the times require. This paper hopes to optimize and innovate personalized recommendation. Among the many personalized recommendation technologies, collaborative filtering algorithm is a hot research topic, because it has the most extensive application and the longest development time. The most mature algorithm is to analyze the interests of users and predict what kind of products they may consume in the future according to the historical consumption and scoring data stored in the system database. In order to implement personalized recommendation, the previous collaborative filtering algorithms are mainly based on the user score matrix, the perception of user preferences, With the development of the algorithm, especially the problem of scalability, cold start problem and other bottlenecks, we rely solely on the score matrix data to find the nearest neighbor. Therefore, we must find other effective sources of user preference data. In this paper, we improve the perception method of user preference and introduce user attribute information as an important data source of preference perception. Together with the score matrix, it forms the data base of user preference perception. As an important information describing the individual characteristics of users, different attributes can divide users into different groups. These user groups may have a certain similarity of interest preferences. The common interests of these specific user groups can be found as the basis for producing recommendations. In this paper, a new parameter to measure user interest preference is defined. That is: interest contrast. On this basis, a collaborative filtering personalized recommendation algorithm combining user attributes and interest contrast is proposed. After sorting and deleting, the final recommendation list is formed. The new algorithm proposed in this paper aims to overcome the scalability problem of traditional collaborative filtering. In the whole process design of the new algorithm, We do not rely on the traditional user similarity calculation to find the nearest neighbor. Therefore, when the user and project grow rapidly, the algorithm complexity will not rise sharply. The collaborative filtering recommendation algorithm which combines user attributes and interest contrast can achieve satisfactory recommendation quality on the premise of ensuring real-time recommendation. It is a flexible and efficient recommendation scheme. More importantly, it provides a new way of recommendation. In addition, this paper also makes a systematic analysis of the efficiency of recommendation under different attribute combinations, which lays a foundation for the related research in this field.
【學位授予單位】：華中師范大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：F224;F713.36

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