【摘要】：隨著Web3.0技術(shù)逐漸成熟和社會(huì)化媒體技術(shù)日益普及,電子商務(wù)個(gè)性化推薦系統(tǒng)備受關(guān)注并廣泛應(yīng)用且呈現(xiàn)社會(huì)化、移動(dòng)化發(fā)展趨勢(shì),其基于信息推薦機(jī)制,利用電子商務(wù)網(wǎng)站向客戶提供商品的信息和建議,模擬銷(xiāo)售人員輔助用戶進(jìn)行商品購(gòu)買(mǎi)決策。伴隨社會(huì)化電子商務(wù)發(fā)展及Web2.0技術(shù)發(fā)展,商品評(píng)論信息日益豐富,但限于用戶評(píng)分習(xí)慣、現(xiàn)有技術(shù)局限性,傳統(tǒng)基于情感分析的電子商務(wù)個(gè)性化推薦在推薦精準(zhǔn)度、智能性、可擴(kuò)展性等方面存在局限,嚴(yán)重影響用戶使用體驗(yàn),新出現(xiàn)的語(yǔ)義情感分析技術(shù)為解決這些問(wèn)題提供了可能性,基于此,本文選擇基于語(yǔ)義情感分析的電子商務(wù)個(gè)性化推薦進(jìn)行研究。本文創(chuàng)新之處包括:(1)將語(yǔ)義情感分析用于電子商務(wù)個(gè)性化推薦,提高電子商務(wù)個(gè)性化推薦系統(tǒng)精確度、智能性現(xiàn)有將語(yǔ)義情感分析和電子商務(wù)個(gè)性化推薦系統(tǒng)作為單獨(dú)領(lǐng)域,相關(guān)研究成果已較多,但將兩個(gè)領(lǐng)域結(jié)合的研究成果則非常少見(jiàn),故此,本文將兩者結(jié)合起來(lái)構(gòu)建了基于語(yǔ)義情感分析的電子商務(wù)個(gè)性化推薦模型,初步揭示了基于語(yǔ)義情感分析的電子商務(wù)個(gè)性化推薦全貌。(2)提出改進(jìn)后基于語(yǔ)義情感分析的電子商務(wù)用戶興趣建模方法,核心是優(yōu)化后的用戶興趣相似度算法。本文優(yōu)化了傳統(tǒng)基于情感分析的電子商務(wù)個(gè)性化推薦方法,主要是用戶興趣建模中興趣相似度算法。全文共分5章,具體如下:第1章介紹了論文選題背景和研究意義,分析了基于情感分析的信息推薦、電子商務(wù)個(gè)性化推薦、語(yǔ)義情感分析等主題的國(guó)內(nèi)外研究現(xiàn)狀,闡述了論文研究方案、創(chuàng)新點(diǎn)和組織結(jié)構(gòu)等。第2章闡述了電子商務(wù)個(gè)性化推薦及其典型應(yīng)用、關(guān)鍵技術(shù)等,分析了語(yǔ)義情感分析、基于情感分析的信息推薦等理論及相關(guān)技術(shù),為基于語(yǔ)義情感分析的電子商務(wù)個(gè)性化推薦模型設(shè)計(jì)及應(yīng)用案例分析奠定知識(shí)基礎(chǔ)。第3章根據(jù)相關(guān)理論及技術(shù),分析設(shè)計(jì)了基于語(yǔ)義情感分析的電子商務(wù)個(gè)性化推薦模型的設(shè)計(jì)目標(biāo)、遵循的基本原則、設(shè)計(jì)思路,設(shè)計(jì)了基于語(yǔ)義情感分析的電子商務(wù)個(gè)性化推薦模型的體系結(jié)構(gòu)、功能模塊、運(yùn)行機(jī)理、技術(shù)解決方案。第4章從基于語(yǔ)義情感分析的電子商務(wù)個(gè)性化推薦技術(shù)實(shí)現(xiàn)及應(yīng)用角度進(jìn)行分析,并闡述了實(shí)現(xiàn)模型實(shí)現(xiàn)所涉及技術(shù)的基礎(chǔ)性工作、環(huán)境部署、驅(qū)動(dòng)配置,并從所構(gòu)建的基于語(yǔ)義情感分析的電子商務(wù)個(gè)性化推薦模型中的四個(gè)方面:用戶興趣建模、推薦機(jī)制、信息資源管理、語(yǔ)義情感分析,闡述了在餐廳電子商務(wù)個(gè)性化推薦系統(tǒng)的具體實(shí)現(xiàn),供其他相關(guān)應(yīng)用與實(shí)踐參考。第5章總結(jié)了論文研究工作,展望了后續(xù)研究方向。
[Abstract]:With the maturity of Web3.0 technology and the popularity of social media technology, E-commerce personalized recommendation system has been paid attention to and widely used, showing the trend of social and mobile development, which is based on information recommendation mechanism. E-commerce website is used to provide customers with information and advice on products, and to simulate sales personnel to assist users in making purchase decisions. With the development of social electronic commerce and Web2.0 technology, the information of commodity review is more and more abundant, but it is limited to users' scoring habit, the limitation of existing technology, and the traditional recommendation of electronic commerce based on emotion analysis is the accuracy of recommendation. There are limitations in intelligence, scalability and so on, which seriously affect the user's experience. The new semantic emotion analysis technology provides the possibility to solve these problems. In this paper, the semantic emotional analysis based on e-commerce personalized recommendation for research. The innovations of this paper are as follows: (1) the semantic emotional analysis is used in E-commerce personalized recommendation to improve the accuracy of E-commerce personalized recommendation system. Currently, semantic emotional analysis and e-commerce personalized recommendation system are regarded as a separate field in intelligence. There are many related research results, but the research results of combining the two fields are very rare, so, In this paper, a personalized recommendation model of E-commerce based on semantic emotional analysis is constructed by combining the two models. This paper preliminarily reveals the full picture of personalized recommendation of e-commerce based on semantic emotional analysis. (2) an improved modeling method of user interest based on semantic emotional analysis is proposed, the core of which is the optimized similarity algorithm of user interest. This paper optimizes the traditional e-commerce personalized recommendation method based on emotion analysis, which is mainly interest similarity algorithm in user interest modeling. The thesis is divided into five chapters: chapter 1 introduces the background and significance of the thesis, analyzes the research status of information recommendation based on affective analysis, e-commerce personalized recommendation, semantic emotional analysis and other topics at home and abroad. The research scheme, innovation points and organization structure of the thesis are expounded. In chapter 2, the author expatiates on E-commerce personalization recommendation and its typical application, key technology and so on, analyzes the theory and related technology of semantic emotion analysis, information recommendation based on emotion analysis, etc. It lays a knowledge foundation for the design of E-commerce personalized recommendation model based on semantic emotional analysis and application case analysis. In chapter 3, according to the relevant theory and technology, the design goal, the basic principles and the design ideas of the E-commerce personalized recommendation model based on semantic emotional analysis are analyzed and designed. The architecture, function module, operation mechanism and technical solution of the personalized recommendation model of e-commerce based on semantic emotional analysis are designed. Chapter 4 analyzes the realization and application of E-commerce personalized recommendation technology based on semantic emotion analysis, and expounds the basic work, environment deployment, driving configuration of the technology involved in the realization of the model. And from the following four aspects: user interest modeling, recommendation mechanism, information resource management, semantic emotion analysis, This paper expounds the realization of personalized recommendation system in restaurant e-commerce for reference to other related applications and practices. Chapter 5 summarizes the research work of the thesis and looks forward to the future research direction.
【學(xué)位授予單位】：湖北工業(yè)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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