本文選題：移動(dòng)電子商務(wù)　切入點(diǎn)：協(xié)同過濾推薦　出處：《哈爾濱理工大學(xué)》2015年碩士論文

【摘要】：伴隨著無線網(wǎng)絡(luò)的大面積覆蓋、電子商務(wù)的迅猛發(fā)展以及智能手機(jī)的快速普及,使移動(dòng)電子商務(wù)(M-Commerce,MC)在全球范圍內(nèi)迅速成長,其個(gè)性化推薦系統(tǒng)成為提高商家競爭力、滿足用戶個(gè)性化需求的工具。但移動(dòng)電子商務(wù)的特殊性使傳統(tǒng)的推薦系統(tǒng)難以簡單地移植并滿足“數(shù)字宇宙”時(shí)代的特殊需求。移動(dòng)電子商務(wù)是一種情境依賴性較高的移動(dòng)商務(wù)服務(wù),當(dāng)用戶在商品個(gè)性化推薦時(shí)用戶特征、項(xiàng)目特征和用戶的當(dāng)前情境特征都會使用戶興趣產(chǎn)生一定程度的影響。為了滿足移動(dòng)電子商務(wù)的個(gè)性化需求,提高推薦的質(zhì)量,本文在考慮移動(dòng)電子商務(wù)用戶興趣影響因素的基礎(chǔ)上,構(gòu)建了以用戶特征維度、項(xiàng)目特征維度和情境特征維度為變量的三維度用戶興趣模型,并根據(jù)協(xié)同過濾推薦的過程,設(shè)計(jì)了基于用戶興趣的MC協(xié)同過濾推薦方法。該方法首先運(yùn)用加權(quán)的Slope One方法對移動(dòng)電子商務(wù)用戶興趣模型評分?jǐn)?shù)據(jù)進(jìn)行填充,以解決在協(xié)同過濾推薦過程中評分?jǐn)?shù)據(jù)的稀疏性問題。其次,通過基于螢火蟲改進(jìn)的K-means聚類方法,對移動(dòng)電子商務(wù)用戶進(jìn)行聚類,提高了聚類中心選取的準(zhǔn)確性,減少目標(biāo)用戶最近鄰居的搜索空間。最后,通過引入多維相似性理論,改進(jìn)了協(xié)同過濾相似性計(jì)算方法,使用戶之間的相似性度量和推薦更加全面、更加精準(zhǔn),為移動(dòng)電子商務(wù)用戶提供更個(gè)性化和更準(zhǔn)確的推薦結(jié)果。
[Abstract]:With the wide coverage of wireless network, the rapid development of e-commerce and the rapid popularity of smart phones, mobile e-commerce M-Commerce MCS) has grown rapidly in the world, and its personalized recommendation system has become to improve the competitiveness of businesses.A tool to meet the individual needs of a user.However, the particularity of mobile electronic commerce makes it difficult for the traditional recommendation system to simply transplant and meet the special needs of the "digital universe" era.Mobile E-commerce is a highly context-dependent mobile commerce service. User characteristics, project features and users' current situational features will have a certain degree of impact on user interest when users recommend products in a personalized manner.In order to meet the personalized demand of mobile electronic commerce and improve the quality of recommendation, this paper constructs a user characteristic dimension on the basis of considering the influencing factors of user interest in mobile e-commerce.According to the process of collaborative filtering recommendation, a MC collaborative filtering recommendation method based on user interest is designed.Firstly, the weighted Slope One method is used to populate the scoring data of mobile e-commerce users' interest model to solve the problem of sparse rating data in collaborative filtering recommendation process.Secondly, the improved K-means clustering method based on firefly is used to cluster the mobile e-commerce users, which improves the accuracy of the clustering center selection and reduces the search space of the nearest neighbors of the target users.Finally, by introducing multi-dimensional similarity theory, the method of collaborative filtering similarity calculation is improved, which makes the similarity measurement and recommendation between users more comprehensive and accurate.Provide more personalized and accurate recommendation results for mobile e-commerce users.
【學(xué)位授予單位】：哈爾濱理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：F724.6;F274;F724.6

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本文編號：1718124