本文選題：時(shí)空標(biāo)簽 + 協(xié)同過(guò)濾　； 參考：《西安科技大學(xué)》2017年碩士論文

【摘要】：景點(diǎn)推薦服務(wù)平臺(tái)在促進(jìn)旅游業(yè)發(fā)展、推動(dòng)地區(qū)經(jīng)濟(jì)增長(zhǎng)、改善游客出游體驗(yàn)等方面發(fā)揮著不可或缺的作用。為了彌補(bǔ)當(dāng)前主流旅游電子商務(wù)平臺(tái)景點(diǎn)推薦功能缺失的不足以及改善個(gè)性化景點(diǎn)推薦應(yīng)用缺乏的現(xiàn)狀,本文以微博數(shù)據(jù)作為研究與應(yīng)用的基礎(chǔ)數(shù)據(jù),以提出的自學(xué)習(xí)協(xié)同過(guò)濾算法與交集相似度計(jì)算方法作為景點(diǎn)推薦引擎構(gòu)建的理論支撐,以WebGIS技術(shù)、數(shù)據(jù)庫(kù)技術(shù)以及前端開(kāi)發(fā)技術(shù)等作為平臺(tái)設(shè)計(jì)實(shí)現(xiàn)的技術(shù)支持,通過(guò)構(gòu)建時(shí)空標(biāo)簽數(shù)據(jù)模型與景點(diǎn)推薦模型,進(jìn)行推薦算法的評(píng)測(cè)以及平臺(tái)程序的編碼與測(cè)試,完成了南京市景點(diǎn)推薦服務(wù)平臺(tái)的設(shè)計(jì)與實(shí)現(xiàn)。具體研究?jī)?nèi)容與結(jié)果如下:(1)在時(shí)空標(biāo)簽數(shù)據(jù)模型構(gòu)建中,從微博數(shù)據(jù)特征的角度闡述了采用微博數(shù)據(jù)作為研究與應(yīng)用基礎(chǔ)數(shù)據(jù)的可行性,并對(duì)微博數(shù)據(jù)的獲取途徑進(jìn)行了說(shuō)明;詳細(xì)介紹了微博數(shù)據(jù)的聚合處理過(guò)程以及景點(diǎn)、游客、相似景點(diǎn)三個(gè)方面的時(shí)空標(biāo)簽數(shù)據(jù)模型。(2)在景點(diǎn)推薦模型構(gòu)建中,為了改善協(xié)同過(guò)濾存在的數(shù)據(jù)稀疏和新用戶(hù)問(wèn)題,提出了基于文本分詞與標(biāo)簽提取的自學(xué)習(xí)協(xié)同過(guò)濾算法;為了解決傳統(tǒng)相似度度量方法只適用于量化數(shù)值的問(wèn)題,提出了基于特征標(biāo)簽的交集相似度計(jì)算方法;然后對(duì)應(yīng)于基于項(xiàng)目、用戶(hù)以及自學(xué)習(xí)的協(xié)同過(guò)濾構(gòu)建了各自的景點(diǎn)推薦模型。(3)在景點(diǎn)推薦算法評(píng)測(cè)中,分別介紹了評(píng)測(cè)數(shù)據(jù)、評(píng)測(cè)指標(biāo)以及評(píng)測(cè)流程;通過(guò)對(duì)評(píng)測(cè)結(jié)果在準(zhǔn)確率、召回率以及興趣度方面的對(duì)比分析,得出在基于標(biāo)簽的景點(diǎn)推薦中,自學(xué)習(xí)的協(xié)同過(guò)濾明顯優(yōu)于基于項(xiàng)目的協(xié)同過(guò)濾和基于用戶(hù)的協(xié)同過(guò)濾,良好的改善了數(shù)據(jù)稀疏和新用戶(hù)問(wèn)題。(4)在基于WebGIS的景點(diǎn)推薦服務(wù)平臺(tái)設(shè)計(jì)與實(shí)現(xiàn)中,基于自學(xué)習(xí)的協(xié)同過(guò)濾算法和交集相似度計(jì)算方法構(gòu)建了景點(diǎn)推薦引擎,采用GeoDataBase和MongoDB存儲(chǔ)景點(diǎn)空間數(shù)據(jù)和屬性數(shù)據(jù),通過(guò)ArcGIS Server和WCF REST發(fā)布數(shù)據(jù)服務(wù),調(diào)用ArcGIS API、jQuery類(lèi)庫(kù)等進(jìn)行功能實(shí)現(xiàn),利用Html、CSS、Javascript進(jìn)行平臺(tái)用戶(hù)界面的布局與設(shè)計(jì),完成了南京市景點(diǎn)推薦服務(wù)平臺(tái)的設(shè)計(jì)與實(shí)現(xiàn)。
[Abstract]:Recommendation service platform plays an indispensable role in promoting tourism development, promoting regional economic growth and improving tourist experience. In order to make up for the deficiency of the recommendation function of the mainstream tourism e-commerce platform and to improve the status quo of the lack of personalized recommendation application, this paper takes Weibo data as the basic data for research and application. The self-learning collaborative filtering algorithm and the intersection similarity calculation method are used as the theoretical support for the construction of the recommendation engine of scenic spots, and the WebGIS technology, database technology and front-end development technology are used as the technical support for the platform design and implementation. By constructing spatio-temporal label data model and scenic spot recommendation model, evaluating the recommendation algorithm and coding and testing the platform program, the design and implementation of Nanjing Scenic spot recommendation Service platform are completed. The specific research contents and results are as follows: (1) in the construction of spatio-temporal tag data model, the feasibility of using Weibo data as the basic data for research and application is expounded from the point of view of Weibo's data characteristics. This paper introduces in detail the process of data aggregation and processing of Weibo's data, as well as the spatio-temporal label data model of scenic spots, tourists and similar scenic spots. In order to improve the problem of data sparsity and new users in collaborative filtering, this paper discusses the construction of recommendation model for scenic spots. A self-learning collaborative filtering algorithm based on text segmentation and label extraction is proposed, and in order to solve the problem that the traditional similarity measurement method is only applicable to quantization value, an intersection similarity calculation method based on feature labels is proposed. Then, corresponding to the project, user and self-learning collaborative filtering, we construct their recommendation model. In the evaluation of the recommendation algorithm, we introduce the evaluation data, the evaluation index and the evaluation process. Through the comparative analysis of the accuracy, recall and interest of the evaluation results, it is concluded that the self-learning collaborative filtering is better than the project-based collaborative filtering and user-based collaborative filtering in the tag-based recommendation of scenic spots. In the design and implementation of the recommendation service platform based on WebGIS, the recommendation engine is constructed based on self-learning collaborative filtering algorithm and intersection similarity calculation method. Using GeoDataBase and MongoDB to store spatial data and attribute data of scenic spots, publishing data services through ArcGIS Server and WCF REST, calling ArcGIS API jQuery class library, etc. The design and implementation of Nanjing Scenic spot recommendation Service platform are completed.
【學(xué)位授予單位】：西安科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：P208

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