本文選題：社會化推薦 + 社交網(wǎng)站　； 參考：《復旦大學》2014年碩士論文

【摘要】：隨著互聯(lián)網(wǎng)的飛速發(fā)展,全球網(wǎng)民數(shù)量急劇增長�；ヂ�(lián)網(wǎng)世界中,人們在獲取信息的同時也創(chuàng)造著信息,如何為用戶挖掘有用的信息,避免信息過載帶來的不良體驗,成為學術(shù)界和業(yè)界關(guān)注的熱點問題。個性化推薦系統(tǒng)便為解決此類問題應運而生,它旨在分析并挖掘用戶興趣,幫助用戶在大量信息中快速作出決策；或為用戶推薦潛在感興趣的內(nèi)容,從而提升用戶體驗。近年來的社交網(wǎng)絡站點大多已實現(xiàn)了推薦系統(tǒng)的雛形,可為用戶推薦好友或感興趣的內(nèi)容。隨著社交網(wǎng)站用戶規(guī)模和好友網(wǎng)絡的不斷擴大,用戶生成內(nèi)容急劇增多,社交網(wǎng)站中用戶面臨兩個普遍問題：(1)由于信息過載,導致用戶錯過感興趣的話題；(2)由于好友眾多,話題的分享者難以篩選出待分享的目標用戶。協(xié)同過濾技術(shù)(Collaborative Filtering, CF)是迄今為止最成功的個性化推薦技術(shù)之一。由于社交網(wǎng)站自身特性,基于協(xié)同過濾技術(shù)的傳統(tǒng)推薦方法在用于社交網(wǎng)站的推薦時,存在一定的局限性。近些年,基于社交網(wǎng)站的個性化推薦的研究越來越多,大部分的文獻關(guān)注于將社交網(wǎng)站中社會上下文信息建模集成到協(xié)同過濾模型中以改進推薦效果。本文從上述兩個問題出發(fā),基于協(xié)同過濾的基本思想,從聚集相關(guān)用戶的角度將可能錯過的話題推薦給用戶,并為分享者推薦好友列表輔助篩選目標分享用戶,主要工作包括以下幾個方面：·提出一個基于用戶社會活動和好友網(wǎng)絡的推薦算法SoSAN,它結(jié)合用戶之間的關(guān)注度和興趣相似度構(gòu)建用戶之間影響度。SoSAN推薦算法在計算用戶相似度時采用本文基于Jaccard改進的相似性方法,該方法擴大了用戶共同評論行為的權(quán)重�；谡鎸嵣缃痪W(wǎng)絡的實驗分析表明,基于影響度的推薦可提高推薦質(zhì)量,基于Jaccard改進的相似度方法比標準Jaccard表現(xiàn)出更佳效果；·提出一個ComL線性模型,用于為分享者推薦一個好友列表輔助篩選目標好友,它基于分享者的分享習慣和候選好友對分享話題的興趣度構(gòu)建�；谡鎸嵣缃痪W(wǎng)絡的實驗分析表明,ComL可表現(xiàn)出最優(yōu)的命中率；·提出一個可應用于典型社交網(wǎng)站的具有良好通用性的推薦系統(tǒng)框架一AOPUT,它包含兩個核心功能：(a)基于SoSAN算法將特定話題推薦給對話題感興趣的用戶：(b)基于ComL模型為分享者推薦待分享的目標用戶。對該框架的主要組件、工作流程、數(shù)據(jù)模型及算法設計進行了詳細介紹,并分析了框架的通用性和響應性能。
[Abstract]:With the rapid development of the Internet, the number of Internet users in the world has increased dramatically. In the Internet world, people not only obtain information but also create information. How to mine useful information for users and avoid the bad experience caused by information overload has become a hot issue in academia and industry. In order to solve this kind of problems, personalized recommendation system arises at the historic moment. It aims to analyze and excavate users' interests, help users to make decisions quickly in a large amount of information, or recommend content of potential interest to users so as to enhance user experience. In recent years, most social network sites have realized the prototype of recommendation system, which can recommend friends or content of interest to users. With the continuous expansion of user size and friend network of social networking sites, user-generated content has increased dramatically. Users in social networking sites face two common problems: (1) users miss topics of interest due to information overload; (2) because of the number of friends, users miss topics of interest. Topic sharers find it difficult to screen target users to share. Collaborative filtering (CF) is one of the most successful personalized recommendation technologies. Because of its own characteristics, the traditional recommendation method based on collaborative filtering technology has some limitations when it is used for social networking site recommendation. In recent years, there are more and more researches on personalized recommendation based on social networking site. Most of the literatures focus on integrating social context information modeling into collaborative filtering model to improve the recommendation effect. Starting from the above two problems and based on the basic idea of collaborative filtering, this paper recommends the topic that may be missed to the user from the angle of gathering related users, and assists in filtering the target sharing user for the list of recommended friends. The main work includes the following aspects: a recommendation algorithm SoSAN based on user social activities and friend network is proposed, which combines the attention and interest similarity between users to construct the influence degree between users. The similarity method based on Jaccard is used to calculate user similarity. This method expands the weight of user's common comment behavior. The experimental analysis based on real social network shows that the recommendation based on influence degree can improve the quality of recommendation, and the improved similarity method based on Jaccard is more effective than the standard Jaccard, and a ComL linear model is proposed. It is used to recommend a friend list to assist the selection of target friends. It is based on the sharing habits of the sharer and the interest of the candidate friends in the sharing topic. Experimental analysis based on real social network shows that ComL can show the best hit ratio. In this paper, a general recommendation system framework, AOPUTT, which can be applied to typical social networking sites, is proposed. It contains two core functions: (a) based on SoSan algorithm to recommend specific topics to users interested in topics: (b) based on ComL-based The model recommends the target user to be shared for the sharer. The main components, workflow, data model and algorithm design of the framework are introduced in detail, and the generality and response performance of the framework are analyzed.
【學位授予單位】：復旦大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TP393.092

【相似文獻】

相關(guān)期刊論文 前10條

1 ;數(shù)字[J];計算機安全;2008年06期

2 李響;;社交網(wǎng)站:開放中隱藏危險[J];信息網(wǎng)絡;2008年06期

3 樹子;;3年,社交網(wǎng)站擴張[J];互聯(lián)網(wǎng)天地;2008年06期

4 黃亮新;;社交網(wǎng)站隱憂[J];互聯(lián)網(wǎng)天地;2008年06期

5 白潔;;社交網(wǎng)站,想說愛你不容易[J];信息安全與通信保密;2008年10期

6 阿祥;;社交網(wǎng)站的困惑與希望[J];互聯(lián)網(wǎng)天地;2009年03期

7 文小西;;逃臺棄日敗走美國 韓最大社交網(wǎng)站無奈退守本土[J];IT時代周刊;2009年24期

8 譚慧;李世國;;社交網(wǎng)站用戶群體的穩(wěn)定關(guān)系研究[J];產(chǎn)業(yè)與科技論壇;2010年01期

9 ;你不是一個人在生病[J];科技創(chuàng)業(yè);2011年10期

10 王宏昆;;歐美社交網(wǎng)站廣告市場發(fā)展分析[J];青年記者;2012年36期

相關(guān)會議論文 前8條

1 李晨宇;;基于用戶黏性的中國社交網(wǎng)站盈利模式可持續(xù)性分析——以人人網(wǎng)的用戶黏性分析為例[A];中國傳媒大學第五屆全國新聞學與傳播學博士生學術(shù)研討會論文集[C];2011年

2 王洪偉;陳媛;;社交網(wǎng)站用戶持續(xù)使用意向?qū)嵶C研究[A];第十屆中國不確定系統(tǒng)年會、第十四屆中國青年信息與管理學者大會論文集[C];2012年

3 馬曉輝;;青少年的社交網(wǎng)站使用動機[A];第十五屆全國心理學學術(shù)會議論文摘要集[C];2012年

4 李晨宇;;中國社交網(wǎng)站盈利模式的可持續(xù)性分析[A];數(shù)字未來與媒介社會2[C];2010年

5 牛星;;看我國SNS社交網(wǎng)站現(xiàn)狀與趨勢[A];計算機研究新進展（2010）——河南省計算機學會2010年學術(shù)年會論文集[C];2010年

6 李曉娥;;SNS社交網(wǎng)站信息分享行為的影響因素[A];首屆華中地區(qū)新聞與傳播學科研究生學術(shù)論壇獲獎論文[C];2010年

7 姚正宇;;親密關(guān)系與社交網(wǎng)絡互動行為研究[A];“傳播與中國·復旦論壇”（2013）——網(wǎng)絡化關(guān)系：新傳播與當下中國論文集[C];2013年

8 裴馨;杝旖;王妙t$;李思宓;_5伊QR;邢若t，

本文編號：2099210