本文選題：電影推薦模型 + 動(dòng)態(tài)社區(qū)發(fā)現(xiàn)��； 參考：《江蘇大學(xué)》2017年碩士論文

【摘要】：社區(qū)發(fā)現(xiàn)作為一種重要的網(wǎng)絡(luò)分析技術(shù),能夠挖掘出網(wǎng)絡(luò)中具有某些共性的節(jié)點(diǎn)集合。研究網(wǎng)絡(luò)中的社區(qū)對理解整個(gè)網(wǎng)絡(luò)的結(jié)構(gòu)和功能起到至關(guān)重要的作用,它不僅可幫助我們分析及預(yù)測整個(gè)網(wǎng)絡(luò)各元素間的交互關(guān)系,而且可以分析用戶行為以及為用戶提供更加個(gè)性化的搜索結(jié)果�，F(xiàn)實(shí)中,社區(qū)發(fā)現(xiàn)已經(jīng)在多個(gè)領(lǐng)域發(fā)揮著重要作用。本文在深入研究社會(huì)網(wǎng)絡(luò)中社區(qū)發(fā)現(xiàn)算法、Spark相關(guān)技術(shù)以及個(gè)性化推薦技術(shù)的基礎(chǔ)上,提出了基于葉子社區(qū)與節(jié)點(diǎn)比較策略的Louvain算法,并將改進(jìn)的Louvain算法融入Spark Streaming流處理框架,使其能夠動(dòng)態(tài)調(diào)整社區(qū)結(jié)構(gòu),捕捉社區(qū)信息。最后將社區(qū)發(fā)現(xiàn)思想應(yīng)用到個(gè)性化推薦領(lǐng)域中,用于解決大量的向量運(yùn)算等問題。論文的主要工作如下:1.提出了改進(jìn)的Louvain算法,包括葉子社區(qū)策略以及節(jié)點(diǎn)度數(shù)比較策略。其中葉子社區(qū)是指含有葉子節(jié)點(diǎn)且節(jié)點(diǎn)總度數(shù)為2n-1(n為節(jié)點(diǎn)數(shù)目)的社區(qū),葉子社區(qū)策略是指直接將葉子社區(qū)中的節(jié)點(diǎn)劃分到與之相鄰并且度數(shù)小于或者等于2的節(jié)點(diǎn)所在的社區(qū)。節(jié)點(diǎn)度數(shù)比較策略則直接比較相鄰節(jié)點(diǎn)的∑tot的值來找出maxΔQ的鄰居節(jié)點(diǎn)。改進(jìn)算法大量減少ΔQ值計(jì)算,提高了執(zhí)行效率。2.針對Louvain社區(qū)發(fā)現(xiàn)算法僅適應(yīng)于靜態(tài)社會(huì)網(wǎng)絡(luò)的問題,提出了基于Spark Streaming的動(dòng)態(tài)社區(qū)發(fā)現(xiàn)框架(SDCDF),SDCDF中采用的動(dòng)態(tài)社區(qū)發(fā)現(xiàn)策略減少了對整個(gè)網(wǎng)絡(luò)進(jìn)行社區(qū)劃分的次數(shù),提高社區(qū)動(dòng)態(tài)發(fā)現(xiàn)效率。3.針對傳統(tǒng)的電影推薦模型隨用戶與電影數(shù)量的增長導(dǎo)致向量運(yùn)算過于復(fù)雜以及矩陣對系統(tǒng)內(nèi)存開銷大的問題,提出基于Louvain改進(jìn)算法的電影推薦模型(LFRM)。LFRM依據(jù)Louvain改進(jìn)算法的社區(qū)劃分結(jié)果,將用戶-電影矩陣轉(zhuǎn)變成社區(qū)-電影矩陣,再使用ALS訓(xùn)練模型,并進(jìn)行偏好值預(yù)測與電影推薦,該模型通過對用戶-電影矩陣降維來避免大量的向量運(yùn)算,一定程度上提高了個(gè)性化推薦的效率。
[Abstract]:Community discovery, as an important network analysis technology, can excavate some common node sets in the network. The study of communities in the network is crucial to understanding the structure and functions of the network as a whole. It not only helps us analyze and predict the interactions between the elements of the network, It can also analyze user behavior and provide users with more personalized search results. In reality, community discovery has played an important role in many fields. On the basis of deeply studying the community discovery algorithm (Spark) and the personalized recommendation technology in social network, this paper proposes a Louvain algorithm based on the comparison strategy between the leaf community and the node, and integrates the improved Louvain algorithm into the Spark streaming stream processing framework. So that it can dynamically adjust the community structure, capture community information. Finally, the community discovery idea is applied to the field of personalized recommendation, which is used to solve a large number of vector operations and other problems. The main work of the thesis is as follows: 1: 1. An improved Louvain algorithm is proposed, including leaf community strategy and node degree comparison strategy. The leaf community refers to the community with leaf nodes and the total degree of nodes is 2n-1(n as the number of nodes. Leaf community strategy is to divide the nodes in the leaf community directly into the community in which the nodes with a degree of less than or equal to 2 are located. The node degree comparison strategy directly compares the 鈭，

本文編號：2045819