本文選題：協(xié)同過濾　切入點(diǎn)：關(guān)聯(lián)規(guī)則　出處：《西安工業(yè)大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：互聯(lián)網(wǎng)的興起和發(fā)展,而后又帶動(dòng)了電子商務(wù)的飛速發(fā)展。電子商務(wù)將大量的數(shù)據(jù)帶到了我們的面前。面對這些數(shù)據(jù),用戶卻無法從中快速的獲取有效的信息,這就形成了信息超載的問題。搜索引擎在解決這個(gè)問題上取得了一定的成功,例如美國的谷歌和國內(nèi)的百度都是搜索引擎,當(dāng)用戶在搜索引擎中輸入關(guān)鍵詞時(shí),搜索引擎就會(huì)在數(shù)據(jù)庫中尋找用戶可能需要的信息。然而,互聯(lián)網(wǎng)的數(shù)據(jù)浩瀚如海,并非每一個(gè)用戶都能夠明確的知道自己的需求。相比之下,推薦系統(tǒng)則能更好的解決信息超載的問題,也顯得更加的智能和主動(dòng)。推薦系統(tǒng)不需要用戶輸入關(guān)鍵詞,就能夠在海量的數(shù)據(jù)信息中進(jìn)行快速的搜索。另一方面,它會(huì)將用戶可能感興趣的信息主動(dòng)呈現(xiàn)給用戶,也會(huì)根據(jù)不同用戶的不同興趣為用戶提供個(gè)性化推薦服務(wù)。因此,在很多領(lǐng)域中,都能夠看到推薦系統(tǒng)熟悉的身影。尤其在電子商務(wù)網(wǎng)站中有越來越好的發(fā)展前景。推薦算法有多種,但目前主流的并且使用率最高的三個(gè)推薦算法分別是基于內(nèi)容、基于協(xié)同過濾和基于關(guān)聯(lián)規(guī)則的。其中,使用人數(shù)最多、市場占有率最高的推薦算法是第二種算法。然而隨著互聯(lián)網(wǎng)的快速發(fā)展,網(wǎng)站中用戶數(shù)量和產(chǎn)品數(shù)量也在迅速的增長,基于協(xié)同過濾的推薦算法其也暴露出來越來越多亟待解決的問題。其中,最受關(guān)注的問題是數(shù)據(jù)稀疏性和冷啟動(dòng)。如何解決這些問題一直是研究者們的難題,為了使基于協(xié)同過濾的推薦算法有更加良好的推薦效果,本論文提出了利用組合算法來彌補(bǔ)這些缺陷。另外,當(dāng)電子商務(wù)推薦系統(tǒng)中積累了很多數(shù)據(jù)時(shí),單機(jī)處理這些數(shù)據(jù)受到了一定的影響,從而也會(huì)對推薦的準(zhǔn)確度和效率產(chǎn)生影響。因此,對這些數(shù)據(jù)采用分布式處理,使用Hadoop技術(shù)實(shí)現(xiàn)推薦系統(tǒng)中大量數(shù)據(jù)的計(jì)算,提高計(jì)算的效率,將更加準(zhǔn)確的商品推薦給用戶,使用戶越來越依賴電子商務(wù)推薦系統(tǒng),也使得電子商務(wù)網(wǎng)站獲得經(jīng)濟(jì)效益。本論文主要研究了以下幾個(gè)方面:1)對幾種常見的推薦算法做了詳細(xì)的介紹,并描述了各種算法的思想和實(shí)現(xiàn)步驟,同時(shí)了解了各種推薦算法的缺點(diǎn),尤其是協(xié)同過濾推薦系統(tǒng)的不足。2)使用組合算法來解決數(shù)據(jù)稀疏性和冷啟動(dòng)的問題。根據(jù)不同的條件,選用不同的推薦算法。3)使用Hadoop中的MapReduce框架實(shí)現(xiàn)推薦系統(tǒng)海量數(shù)據(jù)的分布式計(jì)算,使推薦系統(tǒng)有更好的性能。
[Abstract]:The rise and development of the Internet, and then the rapid development of e-commerce. Electronic commerce will bring a lot of data to us. In the face of these data, users can not quickly obtain effective information. This has led to the problem of information overload. Search engines have achieved some success in solving this problem. For example, Google in the United States and Baidu in China are both search engines. When users enter keywords in search engines, The search engine will search the database for the information users may need. However, the data on the Internet is so vast that not every user can clearly know their needs. Recommendation system can better solve the problem of information overload, also appear more intelligent and active. Recommendation system does not need user input keywords, can quickly search in the mass of data information, on the other hand, It will actively present to the user information that may be of interest to the user, and will also provide personalized referral services to the user according to the different interests of different users. Therefore, in many fields, Especially in e-commerce websites, there are more and more good prospects. There are many kinds of recommendation algorithms, but the three most popular and most popular recommendation algorithms are based on content. Among them, the recommendation algorithm, which has the most users and the highest market share, is the second one. However, with the rapid development of the Internet, the number of users and the number of products in the website are also growing rapidly. The recommendation algorithm based on collaborative filtering also exposes more and more problems that need to be solved urgently. Among them, the most concerned problems are data sparsity and cold startup. How to solve these problems has always been a difficult problem for researchers. In order to make the recommendation algorithm based on collaborative filtering have better recommendation effect, this paper proposes a combination algorithm to make up for these defects. In addition, when there is a lot of data accumulated in the E-commerce recommendation system, The processing of these data on a single machine is affected to some extent, which will also affect the accuracy and efficiency of recommendation. Therefore, distributed processing is used to process these data, and Hadoop technology is used to realize the calculation of a large number of data in recommendation system. Improve the efficiency of computing, more accurate products recommended to users, so that users rely more and more on e-commerce recommendation system, This paper mainly studies the following aspects: 1) introduce several common recommendation algorithms in detail, and describe the ideas and implementation steps of various algorithms. At the same time, we understand the shortcomings of various recommendation algorithms, especially the shortcomings of collaborative filtering recommendation system. 2) using combinatorial algorithms to solve the problem of data sparsity and cold start, according to different conditions, Different recommendation algorithms. 3) using the MapReduce framework in Hadoop to realize the distributed computing of mass data of recommendation system, so that the recommendation system has better performance.
【學(xué)位授予單位】：西安工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：F713.36;TP391.3

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