基于樹型網(wǎng)絡(luò)的多源用戶興趣數(shù)據(jù)融合方法研究
發(fā)布時間：2018-08-19 09:24
【摘要】：隨著電子商務(wù)的發(fā)展,網(wǎng)上購買成為了時下一種主流的購物方式,消費者在面對海量信息時,需要付出大量時間來找尋自身感興趣的商品。在此情況下,個性化推薦系統(tǒng)應(yīng)運而生,被認(rèn)為是一種有效且符合消費者商品需求的營銷方法,它能解決電子商務(wù)網(wǎng)站中消費者購物選擇問題,是目前網(wǎng)絡(luò)信息服務(wù)領(lǐng)域的熱點之一。個性化服務(wù)系統(tǒng)通過分析使用對象的行為信息,來分析消費者個人的興趣差異習(xí)慣,從而提供“一對一”精準(zhǔn)營銷服務(wù)。要實現(xiàn)個性化推薦系統(tǒng),必須建立用戶興趣模型,用戶建模在個性化推薦中處于核心地位,建模的質(zhì)量直接影響到推薦系統(tǒng)的質(zhì)量。對此,通過捕捉多源用戶興趣數(shù)據(jù)并進行數(shù)據(jù)融合,是提高用戶興趣建模質(zhì)量的一條重要途徑。本文研究的目的在于針對B2C網(wǎng)站環(huán)境下,傳統(tǒng)協(xié)同過濾推薦精度不夠高的問題,提出和實現(xiàn)基于用戶樹型網(wǎng)絡(luò)的多源用戶興趣數(shù)據(jù)融合方法,以改善和優(yōu)化原有方法的推薦質(zhì)量。全文主要研究內(nèi)容如下:首先,本文以建模流程為研究視角,從用戶信息收集、信息表示、技術(shù)處理、更新方式四個方面對個性化推薦系統(tǒng)中的用戶興趣模型建立的現(xiàn)有研究成果進行比較分析,將信息收集歸納為信息來源、信息存儲兩個方面,用以獲取建模的信息來源;將信息表示歸納為語義表示、量化表示兩類方法,用以表征具體的用戶興趣偏好;將數(shù)據(jù)處理歸納為兩類技術(shù),即特征詞權(quán)重、聚類技術(shù),用以加工用戶信息而生成用戶興趣模型;將數(shù)據(jù)更新歸納為時間窗口法、遺忘算法、混合模型等三類方法,用以體現(xiàn)模型中的用戶興趣漂移。其次,從用戶購物流程角度出發(fā),總結(jié)出能最大程度反映消費者興趣偏好的4個因子:商品點擊行為、商品收藏行為、放入購物車行為、下單行為。然后具體量化每種指標(biāo)因子的計算,設(shè)置相應(yīng)規(guī)則實現(xiàn)靜態(tài)用戶興趣權(quán)重�？紤]到用戶興趣變化,設(shè)計了隨時間變化的興趣值,彌補了靜態(tài)系統(tǒng)推薦的不足。針對每個個體,進一步把興趣區(qū)分為長期、短期興趣,同時給出不同的指數(shù)衰減方法。通過上述處理,實現(xiàn)了用戶多源興趣數(shù)據(jù)的有效融合,可以更好地提高推薦精度。最后,實驗基于阿里巴巴集團旗下天貓商城提供的真實用戶數(shù)據(jù)集,通過實施數(shù)據(jù)融合,訓(xùn)練得到每個用戶的興趣模型,并計算出每位用戶的長期、短期興趣,以及各自的興趣周期。本文共完成了三組實驗,第一組為探討各指標(biāo)屬性因子值;第二組為周期衰減模型與不區(qū)分興趣周期的指數(shù)衰減模型作預(yù)測精確度對比實驗;第三組為經(jīng)典協(xié)同過濾算法與本文提出的帶周期衰減過濾算法對比實驗。實驗結(jié)果表明,多源用戶興趣數(shù)據(jù)融合的推薦效果優(yōu)于經(jīng)典的協(xié)同過濾推薦效果。
[Abstract]:With the development of electronic commerce, online shopping has become a mainstream shopping method. Consumers need to spend a lot of time to find the goods they are interested in the face of a great deal of information. In this case, personalized recommendation system emerges as the times require, which is considered to be an effective and suitable marketing method to meet the needs of consumers. It can solve the problem of consumer shopping choice in e-commerce websites. It is one of the hot spots in the field of network information service. By analyzing the behavior information of the users, the individualized service system can analyze the consumers' different habits of interest, thus providing the "one to one" precision marketing service. In order to realize personalized recommendation system, user interest model must be established. User modeling is the core of personalized recommendation, and the quality of modeling directly affects the quality of recommendation system. Therefore, it is an important way to improve the quality of user interest modeling by capturing multi-source user interest data and data fusion. The purpose of this paper is to propose and implement a multi-source user interest data fusion method based on user tree network to solve the problem that the recommendation accuracy of traditional collaborative filtering is not high enough in B2C website environment. To improve and optimize the recommended quality of the original method. The main contents of this paper are as follows: firstly, from the perspective of modeling process, this paper focuses on user information collection, information representation, and technology processing. This paper compares and analyzes the existing research results of user interest model in personalized recommendation system from four aspects of updating mode, and summarizes the information collection into two aspects: information source and information storage, in order to obtain the information source of modeling. The information representation is classified into semantic representation and quantitative representation to represent specific user preferences, and the data processing is classified into two kinds of techniques, namely, the weight of feature words, the clustering technique. The user interest model is generated by processing user information, and the data update is summarized into three kinds of methods, such as time window method, forgetting algorithm and hybrid model, to reflect the drift of user interest in the model. Secondly, from the point of view of the user's shopping flow, four factors which can reflect the consumer's interest and preference to the greatest extent are summarized: commodity click behavior, commodity collection behavior, shopping cart behavior, order behavior. Then the calculation of each index factor is quantified and the corresponding rules are set to realize the static user interest weight. Considering the change of user's interest, the interest value changed with time is designed, which makes up for the deficiency of static system recommendation. For each individual, interest is further divided into long term and short term interest, and different exponential decay methods are given. Through the above processing, the effective fusion of user's multi-source interest data can be realized, and the recommendation accuracy can be improved better. Finally, the experiment is based on the real user data set provided by Tmall Mall, which is owned by Alibaba Group. Through the implementation of data fusion, the interest model of each user is trained, and the long-term and short-term interest of each user is calculated. And their respective interest cycles. In this paper, three groups of experiments have been completed, the first group is to discuss the attribute factor value of each index, the second group is to compare the prediction accuracy between the periodic attenuation model and the exponential attenuation model which does not distinguish the period of interest. The third group is a comparative experiment between the classical collaborative filtering algorithm and the periodic attenuation filtering algorithm proposed in this paper. The experimental results show that the recommendation effect of multi-source user interest data fusion is better than that of classical collaborative filtering.
【學(xué)位授予單位】：四川師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：F724.6
本文編號：2191240
[Abstract]:With the development of electronic commerce, online shopping has become a mainstream shopping method. Consumers need to spend a lot of time to find the goods they are interested in the face of a great deal of information. In this case, personalized recommendation system emerges as the times require, which is considered to be an effective and suitable marketing method to meet the needs of consumers. It can solve the problem of consumer shopping choice in e-commerce websites. It is one of the hot spots in the field of network information service. By analyzing the behavior information of the users, the individualized service system can analyze the consumers' different habits of interest, thus providing the "one to one" precision marketing service. In order to realize personalized recommendation system, user interest model must be established. User modeling is the core of personalized recommendation, and the quality of modeling directly affects the quality of recommendation system. Therefore, it is an important way to improve the quality of user interest modeling by capturing multi-source user interest data and data fusion. The purpose of this paper is to propose and implement a multi-source user interest data fusion method based on user tree network to solve the problem that the recommendation accuracy of traditional collaborative filtering is not high enough in B2C website environment. To improve and optimize the recommended quality of the original method. The main contents of this paper are as follows: firstly, from the perspective of modeling process, this paper focuses on user information collection, information representation, and technology processing. This paper compares and analyzes the existing research results of user interest model in personalized recommendation system from four aspects of updating mode, and summarizes the information collection into two aspects: information source and information storage, in order to obtain the information source of modeling. The information representation is classified into semantic representation and quantitative representation to represent specific user preferences, and the data processing is classified into two kinds of techniques, namely, the weight of feature words, the clustering technique. The user interest model is generated by processing user information, and the data update is summarized into three kinds of methods, such as time window method, forgetting algorithm and hybrid model, to reflect the drift of user interest in the model. Secondly, from the point of view of the user's shopping flow, four factors which can reflect the consumer's interest and preference to the greatest extent are summarized: commodity click behavior, commodity collection behavior, shopping cart behavior, order behavior. Then the calculation of each index factor is quantified and the corresponding rules are set to realize the static user interest weight. Considering the change of user's interest, the interest value changed with time is designed, which makes up for the deficiency of static system recommendation. For each individual, interest is further divided into long term and short term interest, and different exponential decay methods are given. Through the above processing, the effective fusion of user's multi-source interest data can be realized, and the recommendation accuracy can be improved better. Finally, the experiment is based on the real user data set provided by Tmall Mall, which is owned by Alibaba Group. Through the implementation of data fusion, the interest model of each user is trained, and the long-term and short-term interest of each user is calculated. And their respective interest cycles. In this paper, three groups of experiments have been completed, the first group is to discuss the attribute factor value of each index, the second group is to compare the prediction accuracy between the periodic attenuation model and the exponential attenuation model which does not distinguish the period of interest. The third group is a comparative experiment between the classical collaborative filtering algorithm and the periodic attenuation filtering algorithm proposed in this paper. The experimental results show that the recommendation effect of multi-source user interest data fusion is better than that of classical collaborative filtering.
【學(xué)位授予單位】：四川師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：F724.6
【參考文獻】
相關(guān)期刊論文 前4條
1 宋江春;沈鈞毅;;一種新的Web用戶群體和URL聚類算法的研究[J];控制與決策;2007年03期
2 汪中;劉貴全;陳恩紅;;一種優(yōu)化初始中心點的K-means算法[J];模式識別與人工智能;2009年02期
3 王有為;許博;衛(wèi)學(xué)啟;凌鴻;;基于用戶訪問序列聚類的網(wǎng)站導(dǎo)航系統(tǒng)[J];系統(tǒng)工程理論與實踐;2010年07期
4 業(yè)寧,李威,梁作鵬,董逸生;一種Web用戶行為聚類算法[J];小型微型計算機系統(tǒng);2004年07期
，本文編號：2191240
本文鏈接：http://sikaile.net/guanlilunwen/yingxiaoguanlilunwen/2191240.html
最近更新
教材專著
