發(fā)布時(shí)間：2018-01-10 08:30

  本文關(guān)鍵詞：基于商業(yè)WiFi廣告?zhèn)�性化推薦算法的研究　出處：《北京交通大學(xué)》2017年碩士論文　論文類(lèi)型：學(xué)位論文

  更多相關(guān)文章： 商業(yè)WiFi 個(gè)性化推薦 協(xié)同過(guò)濾 關(guān)聯(lián) 認(rèn)證廣告

【摘要】：隨著移動(dòng)互聯(lián)網(wǎng)的迅猛發(fā)展和智能終端的普及,人們對(duì)于無(wú)線網(wǎng)絡(luò)的需求越來(lái)強(qiáng)烈,WiFi是移動(dòng)互聯(lián)網(wǎng)的底層流量入口,作為公眾WiFi的一部分商業(yè)WiFi的出現(xiàn)成功彌補(bǔ)了其覆蓋范圍不足的缺陷,在一定程度上滿足了用戶聯(lián)網(wǎng)的需求,同時(shí)作為用戶上網(wǎng)的底層入口,具有很大的潛在價(jià)值�，F(xiàn)如今商業(yè)WiFi無(wú)線認(rèn)證界面只是簡(jiǎn)單的橫幅廣告或者文字廣告,頁(yè)面內(nèi)容變化還需靠人工來(lái)完成,所以更新率低,最重要的是無(wú)法根據(jù)用戶的喜好進(jìn)行個(gè)性化推薦,做不到“千人千面”。一方面用戶的體驗(yàn)度不好,另一方面商場(chǎng)中有那么多的商品無(wú)法做到精準(zhǔn)營(yíng)銷(xiāo),不利于將流量變現(xiàn)。因此研究在商業(yè)WiFi環(huán)境下廣告?zhèn)�性化推薦算法具有重要意義。本文的主要研究工作如下:(1)對(duì)目前主流的推薦算法進(jìn)行了深入研究,分析了各個(gè)算法的推薦原理,并通過(guò)配圖對(duì)原理進(jìn)行了補(bǔ)充。重點(diǎn)分析了協(xié)同過(guò)濾算法,認(rèn)真學(xué)習(xí)了 Cosine、Pearson以及Adjust Cosine相似度算法,詳細(xì)描述了基于項(xiàng)目的協(xié)同過(guò)濾算法的執(zhí)行過(guò)程;(2)提出并分析了一個(gè)新的基于關(guān)聯(lián)的相似度算法。通過(guò)研究分析商業(yè)WiFi環(huán)境下用戶和廣告特征,在協(xié)同過(guò)濾算法的基礎(chǔ)上,提出了一種新的相似度算法,該算法引入了用戶/項(xiàng)目的關(guān)聯(lián)因子。研究并優(yōu)化了評(píng)分預(yù)測(cè)算法,在基于項(xiàng)目的協(xié)同過(guò)濾算法基礎(chǔ)上,對(duì)評(píng)分預(yù)測(cè)算法進(jìn)行了改進(jìn),推薦準(zhǔn)確度更高,使其更適合商業(yè)WiFi的廣告環(huán)境;(3)通過(guò)MAE(平均絕對(duì)誤差)與RMSE(均方根誤差)值來(lái)計(jì)算文中算法的預(yù)測(cè)精確度,完成了基于MovieLens數(shù)據(jù)集的實(shí)驗(yàn),結(jié)果證明了新的相似度算法和改進(jìn)后的評(píng)分預(yù)測(cè)算法在數(shù)據(jù)不同稀疏程度、不同最近鄰個(gè)數(shù)等情況下,對(duì)比傳統(tǒng)協(xié)同過(guò)濾算法具有更好的推薦準(zhǔn)確度;(4)最后,利用本文的算法思路,設(shè)計(jì)了商業(yè)WiFi認(rèn)證廣告系統(tǒng)框架,分析了客戶端與服務(wù)器進(jìn)行通信的網(wǎng)關(guān)協(xié)議,利用當(dāng)前的開(kāi)源路由器固件OpenWRT和認(rèn)證服務(wù)器固件OpenWMS,設(shè)計(jì)并實(shí)現(xiàn)了商業(yè)WiFi的接入認(rèn)證模塊。
[Abstract]:With the popularity of the rapid development of mobile Internet and intelligent terminals, demand for wireless network more and more, WiFi is the underlying traffic entrance of the mobile Internet, as the public part of the WiFi business WiFi make up the defect of inadequate coverage, to a certain extent to meet the needs of Internet users at the same time, the bottom entrance as Internet users, has great potential value. Now the business of WiFi wireless authentication interface simply banner or text ads, the content of the page changes also need to rely on manual to complete, so the update rate is low, the most important thing is not according to the user's preferences for personalized recommendation, do not "Thousand Faces" on the one hand. The user experience is not good, on the other hand, there are so many shopping malls in the goods can do precision marketing, is not conducive to the flow is realized. So the study on commercial WiFi Under the environment of advertising personalized recommendation algorithm has important significance. The main research work of this paper is as follows: (1) the current mainstream recommendation algorithm has been studied, analyzed the principle of each recommendation algorithm, and by drawing on the principle of complementary. Focus on the analysis of the collaborative filtering algorithm, to seriously study the Cosine, Pearson and Adjust Cosine similarity algorithm is described in detail the implementation process of the collaborative filtering algorithm based on project; (2) proposed and analyzed a new similarity algorithm based on correlation analysis. Through the study of households and commercial advertising features under the environment of WiFi, based on collaborative filtering algorithm, proposes a new similarity algorithm. The algorithm introduces the correlation factor user / project. Research and optimization of the score prediction algorithm based on collaborative filtering algorithm based on the project, the score prediction algorithm for the improved push Recommend more accurate, to make it more suitable for commercial WiFi advertising environment; (3) by MAE (mean absolute error) and RMSE (mean square error) value to calculate the prediction accuracy of the algorithm, completed the experiments based on MovieLens data set, the results proved that the new similarity score calculation method and prediction algorithm the improved sparse data in different degree, different nearest neighbor number of cases, compared with the traditional collaborative filtering algorithm has better recommendation accuracy; (4) finally, using the idea of this algorithm, the design of commercial advertising system WiFi authentication framework, gateway protocol analysis of client and server communication, using the current open source router firmware OpenWRT and authentication server firmware OpenWMS, the design and implementation of access authentication module of commercial WiFi.

【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(lèi)號(hào)】：TP391.3;TN92

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