本文選題：推薦系統(tǒng) + 協(xié)同過濾��； 參考：《揚州大學》2016年碩士論文

【摘要】：隨著Wleb技術(shù)在互聯(lián)網(wǎng)中發(fā)展,用戶不再是簡單地從網(wǎng)絡(luò)中獲取信息,而是采取更加主動的方式產(chǎn)生信息。由于用戶數(shù)量的急劇增長,以用戶為中心的信息產(chǎn)生模式,導致了互聯(lián)網(wǎng)信息量呈現(xiàn)飛速增長,這種現(xiàn)象被稱為“信息過載”。該現(xiàn)象是指在海量信息面前,人們無法迅速準確地獲取對他們有用的信息。為了解決“信息過載”問題,推薦系統(tǒng)由此而產(chǎn)生。推薦系統(tǒng)不要求用戶提供準確的需求,而是根據(jù)對用戶的過去行為進行分析,從而推測出用戶在將來可能需要的信息。當前,在眾多推薦技術(shù)中,協(xié)同過濾推薦技術(shù)由于它獨特的優(yōu)點,在電子商務(wù)中取得了廣泛應(yīng)用。雖然協(xié)同過濾推薦算法的研究工作已經(jīng)取得許多成果,但依然存在很多問題亟需解決。比如“冷啟動”、“可擴展性”、“數(shù)據(jù)稀疏性”等問題,這些問題的存在,對算法的準確性造成了影響。如何解決上述問題,改進協(xié)同過濾算法性能,一直是推薦系統(tǒng)中重點研究的課題。論文主要工作如下：第一,針對協(xié)同過濾技術(shù)中存在的“冷啟動”、“可擴展性”問題,提出了結(jié)合用戶屬性聚類的協(xié)同過濾推薦算法ID-CF。該推薦系統(tǒng)通過加入權(quán)重的方法,將基于項目的協(xié)同過濾算法與K—means算法相結(jié)合,顯著提高其推薦準確度。在算法中,由于項目之間的相似性和用戶聚類可以離線計算,這樣可以解決推薦系統(tǒng)的可擴展性問題。當一個新用戶加入系統(tǒng)時,通過使用聚類算法,可將新用戶添加到最相近的用戶集,這樣可以快速預(yù)測用戶對項目的評分,冷啟動問題也可較好地解決。第二,由于“數(shù)據(jù)稀疏性”問題對協(xié)同過濾算法的準確性有較大的影響,提出了一種結(jié)合圖模型的協(xié)同過濾推薦算法NG-CF,該算法提出一種新的相似性度量標準,即用戶或者項目之間的相似性,可以通過圖中頂點之間的關(guān)系來獲得,然后使用K-近鄰算法產(chǎn)生預(yù)測。實驗表明, 即使改變數(shù)據(jù)稀疏性,預(yù)測結(jié)果也具有較好的穩(wěn)定性。“冷啟動”、“可擴展性”、“數(shù)據(jù)稀疏性”等問題是協(xié)同過濾推薦算法研究的熱點問題,論文是在前人的工作的基礎(chǔ)上,僅僅做出一些探索和分析,還有許多問題需要改進。
[Abstract]:With the development of Wleb technology in the Internet, users no longer simply get information from the network, but take a more active way to generate information. Due to the rapid growth of the number of users, the user-centered information generation model leads to the rapid growth of Internet information, which is called "information overload". This phenomenon means that in the face of mass information, people can not get useful information quickly and accurately. In order to solve the problem of information overload, recommendation system is produced. Recommendation system does not require the user to provide accurate requirements, but based on the past behavior of the user to analyze, so as to speculate the user may need information in the future. At present, collaborative filtering recommendation technology has been widely used in e-commerce due to its unique advantages among many recommendation technologies. Although many achievements have been made in collaborative filtering recommendation algorithms, there are still many problems to be solved. Such as "cold start", "extensibility", "data sparsity" and other problems, these problems have an impact on the accuracy of the algorithm. How to solve the above problems and improve the performance of collaborative filtering algorithm has been the focus of research in recommendation system. The main work of this paper is as follows: first, aiming at the problems of "cold start" and "expansibility" in collaborative filtering technology, a collaborative filtering recommendation algorithm ID-CFbased on user attribute clustering is proposed. The recommendation system combines the project-based collaborative filtering algorithm with the K-means algorithm by adding weights to improve the accuracy of recommendation. In the algorithm, due to the similarity between items and user clustering can be calculated offline, this can solve the scalability problem of recommendation system. When a new user joins the system, the new user can be added to the closest user set by using clustering algorithm, which can quickly predict the user's score on the item, and the cold start problem can be solved better. Secondly, because the problem of "data sparsity" has great influence on the accuracy of collaborative filtering algorithm, a collaborative filtering recommendation algorithm NG-CFS combining graph model is proposed, which proposes a new similarity measurement standard. In other words, the similarity between users or items can be obtained by the relationship between vertices in the graph, and then the K-nearest neighbor algorithm is used to generate prediction. The experimental results show that the prediction results are stable even if the data sparsity is changed. "Cold start", "expansibility" and "data sparsity" are hot issues in the research of collaborative filtering recommendation algorithm. Based on the previous work, this paper only makes some exploration and analysis, and many problems need to be improved.
【學位授予單位】：揚州大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TP391.3

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