本文選題：基于位置的社會(huì)網(wǎng)絡(luò)　切入點(diǎn)：位置推薦　出處：《北京交通大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著移動(dòng)互聯(lián)網(wǎng)的快速發(fā)展和移動(dòng)終端設(shè)備的普及,位置服務(wù)(Location Based Service,簡(jiǎn)稱LBS)與傳統(tǒng)社會(huì)網(wǎng)絡(luò)逐漸融合,形成了基于位置的社會(huì)化網(wǎng)絡(luò)(Location-Based Social Network,簡(jiǎn)稱 LBSN)。LBSN 通過用戶的地理位置信息,將線上虛擬社會(huì)網(wǎng)絡(luò)與線下實(shí)體世界聯(lián)系在一起,使得用戶能更方便的分享和獲取感興趣的信息,越來越受到用戶的青睞。LBSN下的位置推薦不僅能幫助用戶發(fā)現(xiàn)其感興趣的新位置,而且還能幫助商家進(jìn)行品牌推廣及精準(zhǔn)營(yíng)銷,從而帶來巨大的經(jīng)濟(jì)效益,具有極大的研究?jī)r(jià)值,已經(jīng)成為了學(xué)術(shù)界和工業(yè)界研究的熱點(diǎn)。雖然大量的線上和線下用戶數(shù)據(jù)的積累為研究LBSN下的位置推薦提供了良好的數(shù)據(jù)基礎(chǔ),但是由于LBSN中的數(shù)據(jù)具有規(guī)模大、多維度、稀疏性高的特點(diǎn),使得現(xiàn)有的位置推薦算法在算法實(shí)時(shí)性、推薦準(zhǔn)確度等方面仍有較大提升空間。針對(duì)上述存在的問題,本文融合了 LBSN中的社交關(guān)系、時(shí)間、空間等多維信息,利用復(fù)雜網(wǎng)絡(luò)鏈路預(yù)測(cè)技術(shù)來進(jìn)行位置推薦,完成的工作及研究成果如下:(1)從社交關(guān)系、時(shí)間、空間三個(gè)方面對(duì)LBSN下的用戶簽到數(shù)據(jù)進(jìn)行深入分析,挖掘出了用戶簽到行為的一般模式。結(jié)合LBSN數(shù)據(jù)的特征,構(gòu)建了一個(gè)包含用戶、位置兩類節(jié)點(diǎn),包含用戶-用戶、用戶-位置、位置-位置三類邊的復(fù)雜圖模型。同時(shí)融合時(shí)空等多維信息,提出了對(duì)圖模型中三類邊的權(quán)值度量方法;(2)提出了基于圖消息傳播的二度好友選取算法GraphSF(Graph Second Friends),能夠過濾圖模型中用于計(jì)算的用戶節(jié)點(diǎn)數(shù)量。在此基礎(chǔ)上,提出了隨機(jī)游走的鏈路預(yù)測(cè)算法WPPR(Weighted PersonalizedPageRank),用復(fù)雜網(wǎng)絡(luò)的鏈路預(yù)測(cè)技術(shù)完成位置推薦。該算法考慮了邊權(quán)值的影響,并加入了重啟機(jī)制,使得其具有較好的推薦準(zhǔn)確性和運(yùn)行效率;(3)基于Spark平臺(tái)下的并行圖計(jì)算框架GraphX對(duì)本文提出的算法進(jìn)行了并行化實(shí)現(xiàn),有效的提高了算法的可擴(kuò)展性和實(shí)時(shí)性能。最后在真實(shí)的Spark集群環(huán)境下與其他幾類位置推薦算法進(jìn)行對(duì)比實(shí)驗(yàn),結(jié)果表明本文提出的算法不僅在準(zhǔn)確率和召回率指標(biāo)上表現(xiàn)良好,而且算法效率更高,擴(kuò)展性更強(qiáng);(4)以本文提出的鏈路預(yù)測(cè)位置推薦算法作為推薦引擎,結(jié)合Google Map API及web開發(fā)相關(guān)技術(shù),實(shí)現(xiàn)了一個(gè)LBSN下的位置推薦原型系統(tǒng)。
[Abstract]:With the rapid development of mobile Internet and the popularity of mobile terminal devices, location Based Service (LBSs) and traditional social networks are gradually merged, and a location-based social network based Social network is formed, which is referred to as LBSN).LBSN through the geographic location information of users. Connecting online virtual social networks with offline physical worlds allows users to more easily share and access information of interest. The location recommendation under. LBSN is becoming more and more popular among users. It can not only help users find the new location that they are interested in, but also help merchants to promote brand and accurate marketing, which brings great economic benefits and has great research value. Although the accumulation of a large amount of online and offline user data provides a good data base for the research of location recommendation under LBSN, because of the large scale and multi-dimension of the data in LBSN, Because of its high sparsity, the existing location recommendation algorithms still have a great improvement in real-time algorithm, recommendation accuracy and so on. In view of the above problems, this paper combines the social relationship, time and time in LBSN. Space and other multidimensional information, the use of complex network link prediction technology to recommend the location, the completed work and research results are as follows: 1) from the social relations, time, space three aspects of the user sign in LBSN data in-depth analysis, This paper excavates the general pattern of user check-in behavior, combines the characteristics of LBSN data, and constructs two kinds of nodes including user, location, user-user, user-location, and so on. A complex graph model with three edges of position and position. At the same time, it fuses the multidimensional information, such as space-time and so on. In this paper, a method to measure the weights of three kinds of edges in graph model is presented. (2) the second degree friend selection algorithm GraphSF(Graph Second friends based on graph message propagation is proposed, which can filter the number of user nodes used in the graph model to calculate the number of user nodes. A random walk link prediction algorithm, WPPR(Weighted Personalized Page rank, is proposed, which uses the link prediction technique of complex networks to complete the location recommendation. The algorithm takes into account the influence of boundary weights and adds a restart mechanism. It has good recommendation accuracy and running efficiency. Based on the parallel graph computing framework GraphX under Spark platform, the algorithm proposed in this paper has been parallelized. The extensibility and real-time performance of the algorithm are improved effectively. Finally, the algorithm is compared with other location recommendation algorithms in the real Spark cluster environment. The results show that the proposed algorithm not only performs well in accuracy and recall index, but also has higher efficiency and expansibility. Combined with Google Map API and web development technology, a location recommendation prototype system based on LBSN is implemented.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.3

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