本文選題：大數(shù)據(jù) + 電子商務(wù)�。� 參考：《西安工業(yè)大學(xué)》2017年碩士論文

【摘要】：山西大同某游戲公司開發(fā)了具有當(dāng)?shù)靥厣亩嗫钇迮祁愑螒?同時(shí)以游戲?yàn)橐劳卸嘣?jīng)營,提供了免費(fèi)送貨服務(wù)當(dāng)?shù)匕傩盏娜沼闷肪W(wǎng)絡(luò)商城電子商務(wù)平臺,但近年來經(jīng)營出現(xiàn)了用戶數(shù)量停滯、效益逐漸下滑的現(xiàn)象,通過長期考察和了解,發(fā)現(xiàn)用戶的需求和喜好已經(jīng)發(fā)生了變化,如何動(dòng)態(tài)的準(zhǔn)確把握當(dāng)?shù)匕傩盏南M(fèi)需求已經(jīng)成為公司亟需解決的頭等大事,研究適合當(dāng)?shù)匕傩盏拇髷?shù)據(jù)電子商務(wù)推薦系統(tǒng)成為解決此問題的最佳選擇。為滿足用戶需求和提供優(yōu)質(zhì)服務(wù),論文設(shè)計(jì)了基于Hadoop的電子商務(wù)推薦系統(tǒng),推薦系統(tǒng)根據(jù)用戶點(diǎn)擊的路徑分析出用戶的需求,從過去對交易后數(shù)據(jù)分析來發(fā)現(xiàn)用戶需求過渡到現(xiàn)在對交易前的過程數(shù)據(jù)分析,從而挖掘出用戶的個(gè)性化需求,并及時(shí)為每個(gè)用戶精確推薦滿足需求的物品,從而提高銷售業(yè)績,為用戶帶來收益。針對數(shù)據(jù)海量性,推薦要求精確性、及時(shí)性等這些問題,本論文設(shè)計(jì)了基于Hadoop平臺的電子商務(wù)推薦系統(tǒng)整體框架。對大數(shù)據(jù)平臺、推薦算法、電子商務(wù)平臺進(jìn)行了需求分析,采用在Linux系統(tǒng)上搭建Hadoop大數(shù)據(jù)平臺;利用分布式文件存儲(chǔ)系統(tǒng)HDFS和MySQL混合數(shù)據(jù)庫構(gòu)架結(jié)構(gòu)化和非結(jié)構(gòu)化的數(shù)據(jù)存儲(chǔ);采用計(jì)算模型MapReduce構(gòu)架起電子商務(wù)構(gòu)架推薦算法的引擎,并以此實(shí)現(xiàn)基于用戶協(xié)同過濾算法、基于物品協(xié)同過濾算法和混合推薦算法,同時(shí)設(shè)計(jì)和實(shí)現(xiàn)了電子商務(wù)推薦系統(tǒng)功能,并將推薦結(jié)果通過用Tomcat的Web服務(wù)器以電子商務(wù)網(wǎng)站的方式呈現(xiàn)給用戶。當(dāng)前系統(tǒng)已經(jīng)上線試運(yùn)行,未來將在后續(xù)數(shù)據(jù)采集上更豐富些、個(gè)性化商品推薦上更精確、更及時(shí)。
[Abstract]:A certain game company in Datong, Shanxi, has developed a number of chess and card games with local characteristics. At the same time, relying on the diversified operation of the game, it has provided free delivery services to the local people on the e-commerce platform of the commodity network mall. However, in recent years, the number of users has stagnated and the benefits have gradually declined. Through long-term investigation and understanding, it is found that the needs and preferences of users have changed. How to dynamically and accurately grasp the consumption demand of local people has become the top priority of the company. The study of big data E-commerce recommendation system suitable for local people has become the best choice to solve this problem. In order to meet the needs of users and provide high quality services, this paper designs an E-commerce recommendation system based on Hadoop. The recommendation system analyzes the needs of users according to the path clicked by users. From the past analysis of post-transaction data to the transition of user requirements to pre-transaction process data analysis, the personalized needs of users are mined, and timely and accurate items are recommended for each user to meet their needs. In order to improve sales performance, for users to bring revenue. Aiming at the problems of magnanimity of data, accuracy and timeliness of recommendation, the whole framework of E-commerce recommendation system based on Hadoop platform is designed in this paper. The requirements of big data platform, recommendation algorithm and e-commerce platform are analyzed, and then the Hadoop big data platform is built on the Linux system, and the structured and unstructured data storage is constructed by using the distributed file storage system (HDFS) and MySQL mixed database. The engine of E-commerce framework recommendation algorithm is constructed by MapReduce, and the function of E-commerce recommendation system is designed and implemented based on user collaborative filtering algorithm, article collaborative filtering algorithm and mixed recommendation algorithm. The recommended results are presented to the user by using Tomcat's Web server as an e-commerce website. At present, the system has been put into online trial operation, in the future will be more rich in the follow-up data collection, personalized product recommendation more accurate, more timely.
【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】：TP391.3

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