本文選題：社交網(wǎng)絡(luò)　切入點(diǎn)：信息過(guò)濾　出處：《南京郵電大學(xué)》2015年碩士論文

【摘要】：隨著移動(dòng)互聯(lián)網(wǎng)的快速發(fā)展,移動(dòng)社交網(wǎng)絡(luò)日漸成為我們生活中不可缺的一部分,移動(dòng)社交網(wǎng)絡(luò)具有的廣大的用戶(hù)群體、迅捷、開(kāi)放式的信息傳播方式特性對(duì)商品營(yíng)銷(xiāo)而言極具吸引力。但是現(xiàn)有的移動(dòng)社交網(wǎng)絡(luò)缺乏正規(guī)友好的商品宣傳機(jī)制和虛假偽劣商品信息甄別機(jī)制,從而導(dǎo)致移動(dòng)社交網(wǎng)絡(luò)中各種廣告宣傳無(wú)孔不入,廣告信息真假難辨,嚴(yán)重影響正常用戶(hù)的社交體感。為了解決這一問(wèn)題,本文從兩個(gè)方向進(jìn)行解決。一方面,為了解決垃圾廣告信息影響正常用戶(hù)社交的問(wèn)題,文本研究了垃圾信息過(guò)濾相關(guān)現(xiàn)有技術(shù),其中基于機(jī)器學(xué)習(xí)分類(lèi)的過(guò)濾方法正確率高、成本低,特別是其中的SVM分類(lèi)方法。但SVM分類(lèi)方法訓(xùn)練時(shí)間過(guò)長(zhǎng),不能靈活應(yīng)對(duì)數(shù)據(jù)集的變化,針對(duì)此問(wèn)題本文提出了一種改進(jìn)的SVM增量學(xué)習(xí)算法,相比于傳統(tǒng)的SVM增量學(xué)習(xí)算法,在保持準(zhǔn)確率的同時(shí),節(jié)約學(xué)習(xí)時(shí)間,提升學(xué)習(xí)效率。應(yīng)用于垃圾廣告信息過(guò)濾系統(tǒng)之中,獲得較好的過(guò)濾效果。另一方面,通過(guò)研究社交網(wǎng)絡(luò)中商品推薦現(xiàn)有的算法,對(duì)現(xiàn)有推薦方法,結(jié)合本課題實(shí)際需求,重點(diǎn)研究了社交網(wǎng)絡(luò)信息對(duì)于商品推薦的積極意義,提出一種基于社交網(wǎng)絡(luò)的混合推薦系統(tǒng)。以用戶(hù)社會(huì)關(guān)系網(wǎng)絡(luò)為基礎(chǔ),挖掘出與被推薦用戶(hù)興趣相似度最高的N個(gè)用戶(hù)。與此同時(shí),考慮社交網(wǎng)絡(luò)的用戶(hù)消息中包含的用戶(hù)可能的商品需求,對(duì)用戶(hù)的消息進(jìn)行挖掘,將挖掘出的信息應(yīng)用到推薦之中,獲得了較好的推薦效果和用戶(hù)滿(mǎn)意度。最后,展示了本文提出的移動(dòng)社交網(wǎng)絡(luò)信息過(guò)濾及推薦系統(tǒng)在“友信”系統(tǒng)中的實(shí)現(xiàn)以及取得的效果,并分別針對(duì)過(guò)濾算法和推薦算法對(duì)算法性能進(jìn)行仿真分析。測(cè)試結(jié)果表明本文提出的改進(jìn)的SVM增量學(xué)習(xí)算法在垃圾信息過(guò)濾系統(tǒng)中取得了很好的過(guò)濾效果,基于社交網(wǎng)絡(luò)的混合推薦方法也在契合課題系統(tǒng)需求的基礎(chǔ)上獲得了較好的推薦效果。
[Abstract]:With the rapid development of mobile Internet, mobile social network has become an indispensable part of our life. The characteristics of open information dissemination are very attractive to commodity marketing. However, the existing mobile social networks lack of formal and friendly commodity promotion mechanism and the identification mechanism of fake and inferior commodity information. As a result, all kinds of advertising in mobile social networks are ubiquitous, the advertising information is hard to distinguish, and the social sense of normal users is seriously affected. In order to solve this problem, this paper solves this problem from two directions. In order to solve the problem that spam information affects normal users' social interaction, the text studies the existing technologies of spam filtering, in which the filtering method based on machine learning classification has high accuracy and low cost. Especially the SVM classification method, but the training time of SVM classification method is too long, so it is not flexible to deal with the change of data set. In this paper, an improved SVM incremental learning algorithm is proposed, which is compared with the traditional SVM incremental learning algorithm. At the same time, it can save learning time and improve learning efficiency. It can be applied to spam information filtering system to obtain better filtering effect. On the other hand, by studying the existing algorithms of commodity recommendation in social networks, In this paper, the positive significance of social network information for commodity recommendation is mainly studied, and a hybrid recommendation system based on social network is proposed, which is based on user social network. At the same time, considering the possible commodity requirements of users included in the user messages of social networks, the users' messages are mined. The information extracted is applied to the recommendation system, and good recommendation effect and user satisfaction are obtained. Finally, the realization and effect of the mobile social network information filtering and recommendation system proposed in this paper in the "Friends letter" system are shown. The simulation results show that the improved SVM incremental learning algorithm has achieved a good filtering effect in the spam filtering system. The hybrid recommendation method based on social network also meets the requirements of the project system and obtains a good recommendation effect.
【學(xué)位授予單位】：南京郵電大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類(lèi)號(hào)】：TP391.3

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