本文關(guān)鍵詞： 知識(shí)遷移 跨領(lǐng)域推薦 用戶興趣度 知識(shí)模型　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】：在當(dāng)今的互聯(lián)網(wǎng)時(shí)代下,大量信息數(shù)據(jù)的積累使得人們很難迅速準(zhǔn)確地發(fā)現(xiàn)自己所感興趣的內(nèi)容,推薦系統(tǒng)在一定程度上解決了這種信息過載問題,但是傳統(tǒng)的推薦系統(tǒng)難以解決冷啟動(dòng)、數(shù)據(jù)稀疏性問題。隨著互聯(lián)網(wǎng)的普及,不同領(lǐng)域的信息可以共享和互為補(bǔ)充,為解決冷啟動(dòng)問題,跨領(lǐng)域推薦帶來了機(jī)遇。為提高跨領(lǐng)域推薦結(jié)果的準(zhǔn)確性和多樣性,提升跨領(lǐng)域信息資源的利用率,本文提出了兩種基于領(lǐng)域間知識(shí)遷移的跨領(lǐng)域推薦算法。主要工作如下:(1)本文首先對(duì)源領(lǐng)域和目標(biāo)領(lǐng)域的用戶進(jìn)行分析,在領(lǐng)域間有用戶重疊的場(chǎng)景下,提出了基于用戶興趣相似度遷移的跨領(lǐng)域推薦算法(User interest-based transfer,UIT);從用戶角度出發(fā),用戶的興趣度會(huì)在不同領(lǐng)域中體現(xiàn),用戶的好友在不同領(lǐng)域中不盡相同�；诖�,可通過用戶的好友將用戶在源領(lǐng)域中的興趣度遷移到目標(biāo)推薦領(lǐng)域中去,我們首先把源領(lǐng)域的信息評(píng)分矩陣進(jìn)行填充,再利用矩陣分解方法計(jì)算用戶興趣度,最終我們得到源領(lǐng)域中的興趣度與目標(biāo)域中改進(jìn)相似度的融合算法。(2)針對(duì)領(lǐng)域間沒有用戶重疊的場(chǎng)景,我們進(jìn)一步提出了基于共享知識(shí)模型的跨領(lǐng)域推薦算法(Sharing knowledge pattern,SKP),通過分析各個(gè)領(lǐng)域中用戶-項(xiàng)目-評(píng)分?jǐn)?shù)據(jù),可以得到用戶的潛在特征和項(xiàng)目的潛在特征,在將用戶和項(xiàng)目的潛在特征分別聚類的基礎(chǔ)上,得到用戶分組對(duì)項(xiàng)目分組的評(píng)分知識(shí)模型,最終充分利用目標(biāo)領(lǐng)域的個(gè)性知識(shí)模型和共享各個(gè)領(lǐng)域的共性知識(shí)模型來提供最終的推薦結(jié)果。(3)在Spark集群環(huán)境下,我們對(duì)本文提出的算法以及相關(guān)對(duì)比算法進(jìn)行了并行化實(shí)現(xiàn)和優(yōu)化。實(shí)驗(yàn)結(jié)果表明,與單一領(lǐng)域的協(xié)同過濾算法和目前的跨領(lǐng)域算法相比,本文提出的算法有較低的RMSE,較高的準(zhǔn)確率、召回率和F1值,并且在不同的Spark集群節(jié)點(diǎn)數(shù)量下驗(yàn)證了本文提出的算法更具可擴(kuò)展性和實(shí)時(shí)性。
[Abstract]:In the current Internet era, the accumulation of a large amount of information data makes it difficult for people to quickly and accurately find the content they are interested in. The recommendation system solves the problem of this kind of information overload to a certain extent. However, the traditional recommendation system is difficult to solve the problem of cold start and data sparsity. With the popularization of the Internet, information in different fields can be shared and supplemented each other, in order to solve the cold start problem, Cross-domain recommendation brings opportunities. In order to improve the accuracy and diversity of cross-domain recommendation results, improve the utilization of cross-domain information resources, In this paper, two cross-domain recommendation algorithms based on inter-domain knowledge migration are proposed. In this paper, a cross-domain recommendation algorithm based on user interest similarity migration is proposed. From the user's point of view, the user's interest will be reflected in different fields, and the user's friends will be different in different fields. The user's interest in the source domain can be transferred to the target recommendation field through the friends of the user. We first fill in the information scoring matrix of the source domain, and then calculate the user's interest by using matrix decomposition method. Finally, we get the fusion algorithm of interest degree in source domain and improved similarity degree in target domain. We further propose a cross-domain recommendation algorithm based on shared knowledge model, which is sharing knowledge patternSKP. By analyzing the user-project-score data in each domain, we can get the potential features of users and the potential features of projects. On the basis of clustering the potential features of users and projects, the scoring knowledge model of user groups for project grouping is obtained. Finally, we make full use of the individual knowledge model of the target domain and the common knowledge model of each domain to provide the final recommendation result. (3) in the Spark cluster environment, We parallelize and optimize the proposed algorithm and the correlation contrast algorithm. The experimental results show that, compared with the single domain collaborative filtering algorithm and the current cross-domain algorithm, The proposed algorithm has lower RMSE, higher accuracy, recall rate and F1 value, and it is verified that the proposed algorithm is more scalable and real-time under different number of Spark cluster nodes.
【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

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相關(guān)期刊論文 前1條

1 張亮;柏林森;周濤;;基于跨電商行為的交叉推薦算法[J];電子科技大學(xué)學(xué)報(bào);2013年01期

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本文編號(hào)：1509504