本文選題：協(xié)同過(guò)濾 + 用戶(hù)消費(fèi)水平　； 參考：《東北師范大學(xué)》2016年碩士論文

【摘要】：近幾年,電子商務(wù)這一新興的購(gòu)物模式伴隨著互聯(lián)網(wǎng)技術(shù)的飛速進(jìn)步逐步興起,成為在人群中十分風(fēng)行的一種新型購(gòu)物渠道。相較于傳統(tǒng)的線下購(gòu)物方式,電子商務(wù)具有非常多的優(yōu)勢(shì),一方面由于具有龐大的網(wǎng)絡(luò)用戶(hù),它可以給企業(yè)帶來(lái)更多的營(yíng)業(yè)利潤(rùn),另一方面由于用戶(hù)足不出戶(hù)就可以隨意瀏覽國(guó)內(nèi)國(guó)外,各種各樣琳瑯滿(mǎn)目的商品信息,電子商務(wù)可以帶給使用者更加便捷更加舒適的消費(fèi)體驗(yàn)。與此同時(shí),我們依然不能忽視現(xiàn)有的電子商務(wù)推薦系統(tǒng)中顯現(xiàn)出的一些弊端。用戶(hù)往往在很多時(shí)候不能從極其龐大的商品海洋中準(zhǔn)確地找到本人中意的商品。在這種情況下,電子商務(wù)針對(duì)用戶(hù)的個(gè)性化推薦成為處理這一問(wèn)題的一個(gè)非常有效之辦法。目前各種各樣有關(guān)于推薦方法的研究層出不窮。即便這樣仍會(huì)存在諸如數(shù)據(jù)稀疏,冷啟動(dòng),算法可擴(kuò)展性差之類(lèi)的難題。如何突破這些技術(shù)瓶頸成為現(xiàn)在研究中的重點(diǎn)和難點(diǎn)。協(xié)同過(guò)濾算法是在個(gè)性化推薦上使用的最為廣泛的一項(xiàng)技術(shù),目前基于協(xié)同過(guò)濾算法的研究主要是基于用戶(hù)-項(xiàng)目評(píng)分這一角度來(lái)進(jìn)行各種各樣的改進(jìn)。本文認(rèn)為除了從評(píng)分這一方面來(lái)衡量用戶(hù)間的相似性,還可以利用用戶(hù)自身的一些因素來(lái)分析用戶(hù)的購(gòu)物習(xí)慣。因此,本文的設(shè)想是把現(xiàn)有協(xié)同過(guò)濾推薦的推薦過(guò)程與用戶(hù)消費(fèi)水平因素進(jìn)行融合,認(rèn)為具有不同消費(fèi)水平的用戶(hù)群具備不同的商品傾向性。本文利用用戶(hù)背景信息以及購(gòu)物記錄建立用戶(hù)的二級(jí)消費(fèi)水平模型,對(duì)評(píng)分矩陣進(jìn)行降維處理并對(duì)空缺項(xiàng)目評(píng)分預(yù)測(cè)評(píng)分值。然后結(jié)合用戶(hù)消費(fèi)水平和評(píng)分?jǐn)?shù)據(jù)得到綜合的用戶(hù)相似性,從根據(jù)消費(fèi)水平篩選后的用戶(hù)集中確定目標(biāo)用戶(hù)的最近鄰居集,最后在目標(biāo)用戶(hù)最近鄰居集的基礎(chǔ)上產(chǎn)生推薦項(xiàng)目集。本文在最后通過(guò)觀察實(shí)驗(yàn)驗(yàn)證得到的數(shù)據(jù)結(jié)果,將本文的改進(jìn)效果和傳統(tǒng)的協(xié)同過(guò)濾進(jìn)行對(duì)比,實(shí)驗(yàn)證明結(jié)合用戶(hù)消費(fèi)水平的改進(jìn)推薦算法可以在傳統(tǒng)推薦算法的基礎(chǔ)上為用戶(hù)更加準(zhǔn)確的推薦傾向商品,并且在一定程度上緩解數(shù)據(jù)稀疏性問(wèn)題以及新用戶(hù)問(wèn)題,對(duì)于電子商務(wù)推薦系統(tǒng)的改進(jìn)有一定促進(jìn)作用。
[Abstract]:In recent years, with the rapid progress of Internet technology, E-commerce, a new shopping mode, has become a popular new shopping channel in the crowd. Compared with the traditional offline shopping mode, e-commerce has many advantages. On the one hand, it can bring more business profits to enterprises because of its huge network users. On the other hand, because users can browse at home and abroad without leaving home, various kinds of commodity information, electronic commerce can bring users more convenient and more comfortable consumption experience. At the same time, we still can not ignore the existing e-commerce recommendation system in the emergence of some drawbacks. Users are often unable to find their favorite goods accurately from the vast ocean of goods. In this case, e-commerce personalized recommendation for users becomes a very effective way to deal with this problem. At present, there are a variety of recommendations for the study of the endlessly. Even so, there will still be problems such as sparse data, cold start, poor scalability of algorithms and so on. How to break through these technical bottlenecks has become the focus and difficulty of current research. Collaborative filtering algorithm is the most widely used technology in personalized recommendation. At present, the research based on collaborative filtering algorithm is mainly based on the user-item score to carry out a variety of improvements. This paper holds that apart from measuring the similarity of users from the aspect of score, we can also use some factors of users to analyze their shopping habits. Therefore, the assumption of this paper is to merge the current recommendation process of collaborative filtering recommendation with the factors of user consumption level, and consider that users with different consumption levels have different propensity of goods. Based on the user background information and shopping records, this paper establishes a two-level consumption level model for users, then reduces the dimension of the score matrix and predicts the score value of the vacancy items. Then combined with the user consumption level and the score data to get the comprehensive user similarity, from the user set selected according to the consumption level to determine the target user's nearest neighbor set. Finally, the recommended item set is generated on the basis of the nearest neighbor set of the target user. At the end of this paper, the improved results are compared with the traditional collaborative filtering. The experimental results show that the improved recommendation algorithm combined with the user consumption level can more accurately recommend products for the user on the basis of the traditional recommendation algorithm, and to some extent alleviate the problem of data sparsity and the problem of new users. It can promote the improvement of e-commerce recommendation system.
【學(xué)位授予單位】：東北師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類(lèi)號(hào)】：TP391.3

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