【摘要】：計(jì)算機(jī)技術(shù)的發(fā)展使得我們的社會(huì)進(jìn)入了信息時(shí)代,信息時(shí)代使我們的生活發(fā)生了翻天地覆的變化,我們隨時(shí)隨地都可以在互聯(lián)網(wǎng)中查找自己需要的信息。信息時(shí)代在為我們帶來方便的同時(shí),也帶來了一些問題,那就是所謂的“信息過載”問題�！靶畔⑦^載”是信息化發(fā)展過程中所產(chǎn)生的負(fù)面影響之一,是指在信息化建設(shè)的過程中,由于網(wǎng)絡(luò)中信息量的指數(shù)級(jí)增長(zhǎng),導(dǎo)致信息在網(wǎng)絡(luò)中存在大量的冗余,無法被人們充分利用。為了解決這一問題,研究人員提出了很多方法,其中最具代表性的就是推薦系統(tǒng)了。推薦系統(tǒng)通過對(duì)用戶的歷史數(shù)據(jù)和行為信息進(jìn)行科學(xué)的運(yùn)算、處理、分析,建立用戶的興趣模型,并通過興趣模型對(duì)用戶推薦可能喜歡的內(nèi)容。雖然推薦系統(tǒng)可以有效地解決“信息過載”,但也不可避免地面臨很多問題(如冷啟動(dòng),推薦精度和用戶興趣時(shí)變問題等)。因此,本文主要研究如何提高推薦系統(tǒng)的性能,解決推薦系統(tǒng)的冷啟動(dòng)和用戶興趣時(shí)變問題。針對(duì)時(shí)間對(duì)用戶興趣變化的影響,本文通過分析用戶在網(wǎng)絡(luò)活動(dòng)中的整體行為對(duì)推薦系統(tǒng)的影響,提出了標(biāo)簽活躍周期的概念,標(biāo)簽活躍周期可以很好的反映用戶的行為對(duì)推薦系統(tǒng)的影響。同時(shí)分析用戶添加標(biāo)簽時(shí)間對(duì)整體推薦的影響,從而提出了標(biāo)簽時(shí)間加權(quán)因子。結(jié)合基于網(wǎng)絡(luò)結(jié)構(gòu)的推薦技術(shù)特點(diǎn),應(yīng)用時(shí)間加權(quán)因子對(duì)網(wǎng)絡(luò)結(jié)構(gòu)推薦算法進(jìn)行改進(jìn),提出了一種新的基于時(shí)間權(quán)值的個(gè)性化推薦算法。并將該算法與一些經(jīng)典的算法進(jìn)行比較分析,結(jié)果顯示該算法能夠在Delicious和Movielens數(shù)據(jù)集得到令人滿意的結(jié)果,有效的提高了推薦系統(tǒng)的精度、多樣性。在進(jìn)一步的實(shí)驗(yàn)中發(fā)現(xiàn),基于時(shí)間權(quán)值的個(gè)性化推薦算法在兩個(gè)數(shù)據(jù)集中,當(dāng)資源對(duì)象的權(quán)值越小時(shí),該算法表現(xiàn)越好,結(jié)果也證明了本文提出的算法可以很好地解決“冷啟動(dòng)”問題。
[Abstract]:With the development of computer technology, our society has entered the information age, and the information age has changed our lives all over the world. We can find the information we need in the Internet anytime and anywhere. The information age not only brings us convenience, but also brings some problems, that is, the so-called "information overload" problem. "Information overload" is one of the negative effects in the process of information development. It means that in the process of information construction, due to the exponential growth of information in the network, there is a large number of redundancy of information in the network. People can't make full use of it. To solve this problem, researchers have proposed many methods, the most representative of which is the recommendation system. The recommendation system carries on the scientific operation, the processing, the analysis, the establishment user's interest model through carries on the scientific operation to the user's historical data and the behavior information, and recommends to the user the content which the user may like through the interest model. Although the recommendation system can effectively solve the "information overload", it is inevitable to face many problems (such as cold start, recommendation accuracy and user interest time-varying problems, etc.). Therefore, this paper mainly studies how to improve the performance of recommendation system and solve the problem of cold start-up and time-varying interest of users. In view of the influence of time on the change of user's interest, this paper analyzes the influence of user's overall behavior in network activities on recommendation system, and puts forward the concept of label active cycle. Label active cycle can well reflect the impact of user behavior on the recommendation system. At the same time, the influence of user tagging time on the overall recommendation is analyzed, and then the label time weighting factor is proposed. Combined with the characteristics of recommendation technology based on network structure, a new personalized recommendation algorithm based on time weight is proposed by using time weighting factor to improve the network structure recommendation algorithm. The algorithm is compared with some classical algorithms, and the results show that the algorithm can get satisfactory results in Delicious and Movielens data sets, and improve the accuracy and diversity of the recommendation system effectively. In further experiments, it is found that the personalized recommendation algorithm based on time weights is in two data sets, and the smaller the weight of the resource object is, the better the performance of the algorithm is. The results also show that the algorithm proposed in this paper can solve the problem of "cold start" well.
【學(xué)位授予單位】：哈爾濱理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.3

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 王興國(guó);;基于協(xié)同過濾的推薦算法研究[J];無線互聯(lián)科技;2016年03期

2 李芳;王松;;電子商務(wù)個(gè)性化推薦的發(fā)展與應(yīng)用評(píng)析[J];電子商務(wù);2015年12期

3 黃瑩;宋偉偉;鄧春玲;江曉蘇;;協(xié)同過濾算法在電影推薦系統(tǒng)中的應(yīng)用[J];軟件導(dǎo)刊;2015年08期

4 于洪;李俊華;;一種解決新項(xiàng)目冷啟動(dòng)問題的推薦算法[J];軟件學(xué)報(bào);2015年06期

5 方傳霞;閆仁武;;基于Web挖掘的電子商務(wù)推薦系統(tǒng)研究[J];電子設(shè)計(jì)工程;2015年11期

6 陶永才;何宗真;石磊;衛(wèi)琳;曹仰杰;;基于加權(quán)動(dòng)態(tài)興趣度的微博個(gè)性化推薦[J];計(jì)算機(jī)應(yīng)用;2014年12期

7 印鑒;王智圣;李琪;蘇偉杰;;基于大規(guī)模隱式反饋的個(gè)性化推薦[J];軟件學(xué)報(bào);2014年09期

8 高明;金澈清;錢衛(wèi)寧;王曉玲;周傲英;;面向微博系統(tǒng)的實(shí)時(shí)個(gè)性化推薦[J];計(jì)算機(jī)學(xué)報(bào);2014年04期

9 史寶明;賀元香;張永;;個(gè)性化信息檢索中用戶興趣建模與更新研究[J];計(jì)算機(jī)應(yīng)用與軟件;2014年03期

10 饒俊陽;賈愛霞;馮巖松;趙東巖;;基于本體結(jié)構(gòu)的新聞個(gè)性化推薦[J];北京大學(xué)學(xué)報(bào)(自然科學(xué)版);2014年01期

相關(guān)碩士學(xué)位論文 前4條

1 劉明;基于聚類和會(huì)話情景的混合推薦算法研究[D];華中科技大學(xué);2013年

2 劉友林;基于網(wǎng)絡(luò)結(jié)構(gòu)的個(gè)性化推薦系統(tǒng)的研究[D];東華大學(xué);2012年

3 張學(xué)勝;面向數(shù)據(jù)稀疏的協(xié)同過濾推薦算法研究[D];中國(guó)科學(xué)技術(shù)大學(xué);2011年

4 張馳;基于混合推薦技術(shù)的個(gè)性化資源推薦模型設(shè)計(jì)與實(shí)現(xiàn)[D];上海交通大學(xué);2010年

，

本文編號(hào)：2440453