本文關鍵詞： 協(xié)同過濾 相似度 平均偏差 推薦 Slope One算法 易物網(wǎng)平臺　出處：《北京交通大學》2017年碩士論文　論文類型：學位論文

【摘要】：隨著互聯(lián)網(wǎng)的快速發(fā)展,人們可以獲取到海量的數(shù)據(jù),這極大地促進了人類的進步。然而,隨著數(shù)據(jù)量的不斷增長,如何在海量的數(shù)據(jù)中捕獲自己感興趣的信息已成為大數(shù)據(jù)分析的研究熱點之一,在這種情況下,個性化推薦技術應運而生。個性化推薦不僅可以提高用戶在有效的時間內發(fā)現(xiàn)自己感興趣信息的效率,同時可以使商戶及時主動的將有用信息提供給用戶。因此,研究個性化推薦算法具有極大的商業(yè)價值和意義,已經(jīng)引起了學術界和商業(yè)界的廣泛關注。因此,本文針對基于用戶的個性化協(xié)同過濾算法進行了研究并將算法應用于構建的易物網(wǎng)交易平臺,主要研究成果包括:(1)針對協(xié)同過濾算法的數(shù)據(jù)稀疏這一問題,本文提出了一種基于項目活躍度的填充算法。該算法對用戶的評分數(shù)據(jù)進行slope one預填充,有效地解決了單一使用用戶評分的個數(shù)來計算用戶相似度數(shù)據(jù)稀疏的問題,填充的方式簡單合理有效。與傳統(tǒng)填充方法相比,所提算法能夠增強數(shù)據(jù)的稀疏性和提高用戶相似度計算的精度。(2)針對協(xié)同過濾算法數(shù)據(jù)稀疏的相似度計算精確度的問題,本文提出了一種基于距離懲罰因子的協(xié)同過濾算法。該算法將用戶間共同評分交集的所有評分距離作為懲罰因子來修正傳統(tǒng)皮爾森相似度,通過對相似度增加距離懲罰因子自適應的調整用戶相似度,改善協(xié)同過濾優(yōu)化算法中用戶間相似度精確度。實驗驗證了所提算法的有效性。(3)針對兒童繪本交易的實際問題,構建了易物網(wǎng)平臺并且將上述提出的協(xié)同過濾算法集成在實際平臺中。該平臺主要包括四個模塊:推薦圖書模塊、圖書交易模塊、會員管理模塊、圖書維護模塊。本文提出的算法成功地應用于該易物網(wǎng)平臺,并實現(xiàn)了推薦模塊具有的基本功能。
[Abstract]:With the rapid development of the Internet, people can obtain a large amount of data, which greatly promotes the progress of human beings. However, with the continuous growth of data. How to capture the interesting information in the massive data has become one of the research hotspots in big data's analysis, in this case. Personalized recommendation technology emerges as the times require. Personalized recommendation can not only improve the efficiency of users to find their interesting information in an effective time. At the same time, it can make merchants provide useful information to users in time. Therefore, the study of personalized recommendation algorithm has great commercial value and significance, and has attracted extensive attention from academia and business circles. In this paper, the personalized collaborative filtering algorithm based on users is studied and applied to the tradeoff platform of barter net. The main research results include: 1) sparse data for collaborative filtering algorithm. In this paper, a filling algorithm based on item activity is proposed, which prepopulates the user's rating data with slope one. It effectively solves the problem of using the number of user scores to calculate the sparse data of user similarity, and the filling method is simple, reasonable and effective, compared with the traditional filling method. The proposed algorithm can enhance the sparsity of data and improve the accuracy of user similarity calculation. In this paper, a cooperative filtering algorithm based on distance penalty factor is proposed, which uses all the scoring distances of the common score intersection among users as penalty factors to modify the traditional Pearson similarity. The user similarity is adjusted adaptively by increasing the distance penalty factor to the similarity. Improve the accuracy of user similarity in collaborative filtering optimization algorithm. Experimental results show that the proposed algorithm is effective. The platform is composed of four modules: recommended book module, book transaction module and member management module. The algorithm proposed in this paper has been successfully applied to the platform and realized the basic functions of the recommendation module.
【學位授予單位】：北京交通大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.3

【參考文獻】

相關期刊論文 前1條

1 鄧愛林,左子葉,朱揚勇;基于項目聚類的協(xié)同過濾推薦算法[J];小型微型計算機系統(tǒng);2004年09期

相關博士學位論文 前1條

1 郭艷紅;推薦系統(tǒng)的協(xié)同過濾算法與應用研究[D];大連理工大學;2008年

，

本文編號：1470384