本文選題：推薦系統(tǒng)　切入點(diǎn)：協(xié)同過(guò)濾　出處：《湘潭大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著互聯(lián)網(wǎng)技術(shù)和海量數(shù)據(jù)處理技術(shù)的發(fā)展,人們已經(jīng)進(jìn)入了一個(gè)新的時(shí)代——從以前的信息缺乏來(lái)到了信息過(guò)量的時(shí)代。這種轉(zhuǎn)變也給人們帶來(lái)了巨大的難題:一方面對(duì)用戶來(lái)說(shuō),如何從海量信息中找到自己的需要的、感興趣的信息成為了一個(gè)挑戰(zhàn);另一方面對(duì)于商家來(lái)說(shuō),如何推銷自己的商品,吸引住用戶的眼球,也是一個(gè)值得研究的問(wèn)題。為了解決上述難題,推薦系統(tǒng)應(yīng)運(yùn)而生了,成了商家與用戶之間的橋梁,其目標(biāo)就是為用戶和商家信息提供更便利、更快捷的服務(wù),不僅是用戶能夠發(fā)現(xiàn)感興趣的東西,也能使商家把自己的商品推銷出去,展現(xiàn)給需要的用戶。推薦系統(tǒng)的這種作用和意義,在電子商務(wù)網(wǎng)站中尤為重要。因此,研究推薦算法在電子商務(wù)中的應(yīng)用,將非常有科研意義。論文針對(duì)基于項(xiàng)目(Item-based)的協(xié)同過(guò)濾算法沒(méi)有考慮時(shí)間權(quán)重這一問(wèn)題,提出了增添時(shí)間考慮因素的改進(jìn)型Item協(xié)同推薦算法,設(shè)計(jì)了基于時(shí)間權(quán)重的協(xié)同過(guò)濾電子商務(wù)推薦系統(tǒng)模型,以購(gòu)書(shū)網(wǎng)站為應(yīng)用背景,設(shè)計(jì)一個(gè)購(gòu)物網(wǎng)站推薦模型,并對(duì)其性能進(jìn)行了驗(yàn)證。本論文首先重點(diǎn)研究并對(duì)比了基于用戶(User-based)的協(xié)同過(guò)濾算法和基于項(xiàng)目(Item-based)的協(xié)同過(guò)濾算法。然后提出將時(shí)間因素加入到推薦系統(tǒng)中并作為時(shí)間權(quán)重參與計(jì)算,這樣可以更接近用戶最新的興趣狀態(tài)。對(duì)基于項(xiàng)目和評(píng)分的相似度計(jì)算公式進(jìn)行了改進(jìn),采用時(shí)間加權(quán)相似度計(jì)算公式代替原有公式。并且,為減少時(shí)間開(kāi)銷,算法結(jié)合了數(shù)據(jù)挖掘的聚類方法,項(xiàng)目空間先經(jīng)過(guò)聚類來(lái)降低計(jì)算時(shí)間和空間開(kāi)銷,增加計(jì)算效率。最后,將改進(jìn)的“基于項(xiàng)目?jī)?nèi)容和評(píng)分的時(shí)間型加權(quán)協(xié)同過(guò)濾算法”進(jìn)行仿真實(shí)驗(yàn),實(shí)驗(yàn)結(jié)果表明,改進(jìn)算法在平均絕對(duì)偏差和平均消耗時(shí)間上均優(yōu)于原基于項(xiàng)目的協(xié)同過(guò)濾算法,驗(yàn)證了改進(jìn)算法的高效性。最后進(jìn)行了基于協(xié)同過(guò)濾的電子商務(wù)推薦系統(tǒng)模型設(shè)計(jì)。以圖書(shū)商城購(gòu)書(shū)網(wǎng)站作為推薦模型的實(shí)際應(yīng)用背景,設(shè)計(jì)了購(gòu)物網(wǎng)站個(gè)性化推薦模型,主要內(nèi)容包括:推薦模型的體系結(jié)構(gòu)設(shè)計(jì)、主要功能分析、推薦流程分析、各個(gè)功能模塊的設(shè)計(jì)以及后臺(tái)數(shù)據(jù)庫(kù)設(shè)計(jì)等。
[Abstract]:With the development of Internet technology and massive data processing technology, People have entered a new era-from a lack of information to an era of information overload. This change has also brought people a huge problem: on the one hand, for users, how to find their own needs from the mass of information, Information of interest has become a challenge; on the other hand, how to promote their products and attract the attention of users is also a problem worth studying. In order to solve these problems, the recommendation system came into being. It has become a bridge between merchants and users, whose goal is to provide more convenient and faster services for users and businesses. It is not only that users can discover what they are interested in, but that they can also sell their products. The role and significance of recommendation system is especially important in e-commerce websites. Therefore, the application of recommendation algorithm in e-commerce is studied. Aiming at the problem that the time weight is not taken into account in the collaborative filtering algorithm based on item-based, this paper proposes an improved Item collaborative recommendation algorithm which adds time factors. A collaborative filtering E-commerce recommendation system model based on time weight is designed, and a shopping website recommendation model is designed based on the application background of book purchase website. The performance is verified. Firstly, the collaborative filtering algorithm based on user User-based and the collaborative filtering algorithm based on item-based are studied and compared. Then the time factor is added to the recommendation system and used as a recommendation system. Time weights participate in the calculation, In this way, we can get closer to the latest state of interest of the user. The similarity calculation formula based on item and score is improved, the time-weighted similarity calculation formula is used instead of the original formula, and, in order to reduce the time cost, The algorithm combines the clustering method of data mining, the project space is first clustered to reduce the computation time and space overhead, and increase the computing efficiency. The improved time-weighted collaborative filtering algorithm based on item content and score is simulated. The experimental results show that the improved algorithm is superior to the original item-based collaborative filtering algorithm in terms of average absolute deviation and average consuming time. Finally, the model of E-commerce recommendation system based on collaborative filtering is designed. The personalized recommendation model of shopping website is designed with the book-shopping website as the practical application background. The main contents include: the architecture design of the recommendation model, the main function analysis, the recommendation flow analysis, the design of each function module as well as the background database design and so on.
【學(xué)位授予單位】：湘潭大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TP391.3;F724.6

【參考文獻(xiàn)】

相關(guān)碩士學(xué)位論文 前1條

1 楊焱;基于項(xiàng)目聚類的協(xié)同過(guò)濾推薦算法的研究[D];東北師范大學(xué);2005年

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本文編號(hào)：1588539