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

【摘要】：景點推薦服務(wù)平臺在促進(jìn)旅游業(yè)發(fā)展、推動地區(qū)經(jīng)濟(jì)增長、改善游客出游體驗等方面發(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)平臺景點推薦功能缺失的不足以及改善個性化景點推薦應(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)同過濾算法與交集相似度計算方法作為景點推薦引擎構(gòu)建的理論支撐,以WebGIS技術(shù)、數(shù)據(jù)庫技術(shù)以及前端開發(fā)技術(shù)等作為平臺設(shè)計實現(xiàn)的技術(shù)支持,通過構(gòu)建時空標(biāo)簽數(shù)據(jù)模型與景點推薦模型,進(jìn)行推薦算法的評測以及平臺程序的編碼與測試,完成了南京市景點推薦服務(wù)平臺的設(shè)計與實現(xiàn)。具體研究內(nèi)容與結(jié)果如下:(1)在時空標(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,并對微博數(shù)據(jù)的獲取途徑進(jìn)行了說明;詳細(xì)介紹了微博數(shù)據(jù)的聚合處理過程以及景點、游客、相似景點三個方面的時空標(biāo)簽數(shù)據(jù)模型。(2)在景點推薦模型構(gòu)建中,為了改善協(xié)同過濾存在的數(shù)據(jù)稀疏和新用戶問題,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)值的問題,提出了基于特征標(biāo)簽的交集相似度計算方法;然后對應(yīng)于基于項目、用戶以及自學(xué)習(xí)的協(xié)同過濾構(gòu)建了各自的景點推薦模型。(3)在景點推薦算法評測中,分別介紹了評測數(shù)據(jù)、評測指標(biāo)以及評測流程;通過對評測結(jié)果在準(zhǔn)確率、召回率以及興趣度方面的對比分析,得出在基于標(biāo)簽的景點推薦中,自學(xué)習(xí)的協(xié)同過濾明顯優(yōu)于基于項目的協(xié)同過濾和基于用戶的協(xié)同過濾,良好的改善了數(shù)據(jù)稀疏和新用戶問題。(4)在基于WebGIS的景點推薦服務(wù)平臺設(shè)計與實現(xiàn)中,基于自學(xué)習(xí)的協(xié)同過濾算法和交集相似度計算方法構(gòu)建了景點推薦引擎,采用GeoDataBase和MongoDB存儲景點空間數(shù)據(jù)和屬性數(shù)據(jù),通過ArcGIS Server和WCF REST發(fā)布數(shù)據(jù)服務(wù),調(diào)用ArcGIS API、jQuery類庫等進(jìn)行功能實現(xiàn),利用Html、CSS、Javascript進(jìn)行平臺用戶界面的布局與設(shè)計,完成了南京市景點推薦服務(wù)平臺的設(shè)計與實現(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)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：P208

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