本文選題：協(xié)同過濾　切入點(diǎn)：Top-N推薦　出處：《揚(yáng)州大學(xué)》2017年碩士論文

【摘要】：由于互聯(lián)網(wǎng)中信息爆炸式的增長,導(dǎo)致用戶很難直接發(fā)現(xiàn)有用的信息。為此,根據(jù)用戶的歷史行為數(shù)據(jù)進(jìn)行建模的推薦系統(tǒng)吸引了學(xué)者的廣泛關(guān)注,可以一定程度上緩解信息過載的問題。各種類型的推薦算法中,協(xié)同過濾推薦算法以其可理解性強(qiáng)、無需語義分析等特點(diǎn)成為目前使用最廣泛的推薦算法之一。但是,隨著數(shù)據(jù)規(guī)模的增大,協(xié)同過濾算法面臨著數(shù)據(jù)稀疏,實(shí)時(shí)性以及準(zhǔn)確性和多樣性的權(quán)衡等方面的問題。另外,與之前的評分預(yù)測相比,Top-N推薦形式更符合當(dāng)前協(xié)同過濾推薦算法的需求。因此,本文主要針對協(xié)同過濾算法存在的部分問題,研究協(xié)同過濾算法在Top-N的推薦算法。本文主要的研究工作是:(1)提出了基于兩步預(yù)測的二分網(wǎng)絡(luò)Top-N推薦算法。已有的基于二分網(wǎng)絡(luò)的協(xié)同過濾推薦算法只考慮了用戶選擇,忽略了用戶評分。利用本文的用戶行為中存在的兩階段的行為,提出了基于兩步預(yù)測的二分網(wǎng)絡(luò)Top-N推薦算法。該算法先利用NBI算法預(yù)測用戶對產(chǎn)品進(jìn)行評分的概率,然后利用兩步預(yù)測將其與協(xié)同過濾算法結(jié)合進(jìn)行推薦。在MoiveLens數(shù)據(jù)集上的實(shí)驗(yàn)表明,該算法提高了推薦的準(zhǔn)確度。(2)提出了一種基于屬性比重相似性的兩步預(yù)測Top-N推薦算法。在處理極端不均勻和稀疏的用戶評分?jǐn)?shù)據(jù)時(shí),傳統(tǒng)的協(xié)同過濾推薦算法不能很好的進(jìn)行相似性計(jì)算。為此,考慮用戶對某一類的產(chǎn)品更感興趣和一個(gè)產(chǎn)品可能同屬于不同的屬性,同時(shí),結(jié)合用戶兩階段行為模式,本文提出一種基于屬性比重相似性的兩步預(yù)測Top-N推薦算法。在MoiveLens數(shù)據(jù)集上的實(shí)驗(yàn)表明,算法能提高協(xié)同過濾算法的Top-N推薦的準(zhǔn)確率和多樣性。(3)基于Spark框架的屬性比重相似性的兩步預(yù)測推薦算法的并行化實(shí)現(xiàn)。結(jié)合Spark框架在實(shí)現(xiàn)并行化方面的優(yōu)勢和協(xié)同過濾推薦算法的實(shí)時(shí)性,本文實(shí)現(xiàn)了基于Spark框架的屬性比重相似性的兩步預(yù)測推薦算法的并行化,達(dá)到通過架構(gòu)的方式提高推薦算法實(shí)效性的目標(biāo)。在MoiveLens數(shù)據(jù)集上的實(shí)驗(yàn)結(jié)果表明,該算法能夠提高運(yùn)算的速率。
[Abstract]:Due to the explosive growth of Internet information, it is difficult to directly cause the user to find useful information. Therefore, according to the history data of user recommendation system modeling has attracted wide attention of scholars, can to some extent alleviate the problem of information overload. Various types of recommendation algorithm, collaborative filtering algorithm which can understand strong, no characteristic of semantic analysis has become one of the most widely used recommendation algorithm. However, with the increasing size of the data, the collaborative filtering algorithm is faced with sparse data, real-time and accuracy and diversity trade-off problems. In addition, with the previous score compared to Top-N recommendation form conforms to the current collaborative filtering recommendation algorithm needs. Therefore, this paper focuses on the problems existing collaborative filtering algorithm, research on collaborative filtering algorithm in recommendation algorithm Top-N the. The main research work is: (1) proposed the two network Top-N two step prediction algorithm based on collaborative filtering algorithm. The existing two network only considers the user selection based on ignoring the user score. Using the two stage of the user behavior in the line, put forward two network Top-N two step prediction based algorithm. The algorithm first uses NBI algorithm to predict the user scoring probability of the product, and then use the two step forecast the recommendation combined with collaborative filtering algorithm. Show the experiment on MoiveLens data set, this algorithm can improve the accuracy of the recommendation. (2) proposed a a similar proportion of the two step prediction based on attribute Top-N recommendation algorithm. In dealing with extreme uneven and sparse user rating data, the traditional collaborative filtering algorithm is not very good for similarity calculation. Therefore, consider the use of Users of a particular type of product and more interested in a product may belong to different attributes, at the same time, the two stage combined with user behavior model, this paper presents a similarity based on the attributes of the proportion of two step prediction Top-N recommendation algorithm. Show the experiment on MoiveLens data set, the algorithm can improve the accuracy of collaborative filtering algorithm the Top-N recommendation and diversity. (3) the parallel two step prediction attributes of Spark frame similarity recommendation algorithm based implementation. Combining with the Spark framework in the real-time realization of parallel advantages and collaborative filtering algorithm, this paper realized the parallelization of similar two step predictive recommendation algorithm of attribute the proportion based on the Spark framework, to improve the effectiveness of the algorithm recommended by the target through the architecture. In the MoiveLens data set. The experimental results show that the algorithm can improve the computing speed.

【學(xué)位授予單位】：揚(yáng)州大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.3

【參考文獻(xiàn)】

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本文編號：1698193