本文關(guān)鍵詞： 協(xié)同過(guò)濾 相似度 平均偏差 推薦 Slope One算法 易物網(wǎng)平臺(tái)　出處：《北京交通大學(xué)》2017年碩士論文　論文類型：學(xué)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(jìn)了人類的進(jìn)步。然而,隨著數(shù)據(jù)量的不斷增長(zhǎng),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(diǎn)之一,在這種情況下,個(gè)性化推薦技術(shù)應(yīng)運(yùn)而生。個(gè)性化推薦不僅可以提高用戶在有效的時(shí)間內(nèi)發(fā)現(xiàn)自己感興趣信息的效率,同時(shí)可以使商戶及時(shí)主動(dòng)的將有用信息提供給用戶。因此,研究個(gè)性化推薦算法具有極大的商業(yè)價(jià)值和意義,已經(jīng)引起了學(xué)術(shù)界和商業(yè)界的廣泛關(guān)注。因此,本文針對(duì)基于用戶的個(gè)性化協(xié)同過(guò)濾算法進(jìn)行了研究并將算法應(yīng)用于構(gòu)建的易物網(wǎng)交易平臺(tái),主要研究成果包括:(1)針對(duì)協(xié)同過(guò)濾算法的數(shù)據(jù)稀疏這一問(wèn)題,本文提出了一種基于項(xiàng)目活躍度的填充算法。該算法對(duì)用戶的評(píng)分?jǐn)?shù)據(jù)進(jìn)行slope one預(yù)填充,有效地解決了單一使用用戶評(píng)分的個(gè)數(shù)來(lái)計(jì)算用戶相似度數(shù)據(jù)稀疏的問(wèn)題,填充的方式簡(jiǎn)單合理有效。與傳統(tǒng)填充方法相比,所提算法能夠增強(qiáng)數(shù)據(jù)的稀疏性和提高用戶相似度計(jì)算的精度。(2)針對(duì)協(xié)同過(guò)濾算法數(shù)據(jù)稀疏的相似度計(jì)算精確度的問(wèn)題,本文提出了一種基于距離懲罰因子的協(xié)同過(guò)濾算法。該算法將用戶間共同評(píng)分交集的所有評(píng)分距離作為懲罰因子來(lái)修正傳統(tǒng)皮爾森相似度,通過(guò)對(duì)相似度增加距離懲罰因子自適應(yīng)的調(diào)整用戶相似度,改善協(xié)同過(guò)濾優(yōu)化算法中用戶間相似度精確度。實(shí)驗(yàn)驗(yàn)證了所提算法的有效性。(3)針對(duì)兒童繪本交易的實(shí)際問(wèn)題,構(gòu)建了易物網(wǎng)平臺(tái)并且將上述提出的協(xié)同過(guò)濾算法集成在實(shí)際平臺(tái)中。該平臺(tái)主要包括四個(gè)模塊:推薦圖書(shū)模塊、圖書(shū)交易模塊、會(huì)員管理模塊、圖書(shū)維護(hù)模塊。本文提出的算法成功地應(yīng)用于該易物網(wǎng)平臺(tái),并實(shí)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(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.3

【參考文獻(xiàn)】

相關(guān)期刊論文 前1條

1 鄧愛(ài)林,左子葉,朱揚(yáng)勇;基于項(xiàng)目聚類的協(xié)同過(guò)濾推薦算法[J];小型微型計(jì)算機(jī)系統(tǒng);2004年09期

相關(guān)博士學(xué)位論文 前1條

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

，

本文編號(hào)：1470384