【摘要】：隨著網(wǎng)絡(luò)資源的不斷增長(zhǎng),個(gè)性化推薦系統(tǒng)成為網(wǎng)絡(luò)資源查詢的一種重要的工具,一方面,它可以幫助網(wǎng)絡(luò)用戶節(jié)省網(wǎng)絡(luò)資源搜尋的時(shí)間開(kāi)銷;另一方面,它可以使網(wǎng)絡(luò)用戶在參與度較低的情況下實(shí)現(xiàn)滿意的網(wǎng)絡(luò)資源查找。個(gè)性化推薦系統(tǒng)作為目前的研究熱點(diǎn),國(guó)內(nèi)外學(xué)者對(duì)其進(jìn)行了大量研究,也取得了很大進(jìn)步,但還是存在諸多問(wèn)題。本文針對(duì)個(gè)性化推薦系統(tǒng)中存在的冷啟動(dòng)、準(zhǔn)確度低等問(wèn)題,分析比較了目前常用個(gè)性化推薦算法優(yōu)缺點(diǎn),利用大數(shù)據(jù)對(duì)用戶基本特征屬性元的權(quán)重進(jìn)行分析,實(shí)現(xiàn)對(duì)新用戶行為偏好的合理預(yù)測(cè),并設(shè)計(jì)一種基于用戶的MI(Multiple Instance)聚類算法,提出用戶特征相似度、項(xiàng)目基本特征與項(xiàng)目評(píng)分相似度三者加權(quán)求和的綜合相似度計(jì)算方法,在主觀客觀偏差降到最低的基礎(chǔ)上設(shè)計(jì)了加權(quán)因子的分配方法,通過(guò)實(shí)驗(yàn)驗(yàn)證了其緩解冷啟動(dòng)問(wèn)題和提高推薦準(zhǔn)確度的有效性和優(yōu)越性。針對(duì)數(shù)據(jù)稀疏問(wèn)題,本文通過(guò)用戶信息特征將相似用戶進(jìn)行聚類,為后續(xù)項(xiàng)目評(píng)分?jǐn)?shù)據(jù)統(tǒng)計(jì)平均的進(jìn)行提供一個(gè)有效可信的計(jì)算范圍,再將簇內(nèi)項(xiàng)目評(píng)分?jǐn)?shù)據(jù)的統(tǒng)計(jì)平均值替換缺損值,最后的實(shí)驗(yàn)也表明了此方法對(duì)于解決數(shù)據(jù)稀疏問(wèn)題的有效性。本文實(shí)驗(yàn)數(shù)據(jù)集采用的是MovieLens-ml-100k,該數(shù)據(jù)集包括了訓(xùn)練集和測(cè)試集等,本文最后應(yīng)用該數(shù)據(jù)集對(duì)本文所提算法進(jìn)行實(shí)驗(yàn)分析,驗(yàn)證了本文算法的正確性和優(yōu)越性。
[Abstract]:With the continuous growth of network resources, personalized recommendation system has become an important tool for network resource query. On the one hand, it can help network users to save the time cost of searching network resources; on the other hand, It can make network users realize satisfactory network resource search under the condition of low participation. Personalization recommendation system is a research hotspot at present. Scholars at home and abroad have done a lot of research on it, and have made great progress, but there are still many problems. Aiming at the problems of cold start and low accuracy in the personalized recommendation system, this paper analyzes and compares the advantages and disadvantages of the commonly used personalized recommendation algorithms, and uses big data to analyze the weight of the user's basic characteristic attribute elements. To realize reasonable prediction of new user's behavior preference, and design a user-based MI (Multiple Instance) clustering algorithm, and put forward a comprehensive similarity calculation method, which is weighted summation of user feature similarity, item basic feature and item score similarity. On the basis of minimizing subjective and objective deviations, a weighting factor allocation method is designed, and its effectiveness and superiority in alleviating cold start problem and improving recommendation accuracy are verified by experiments. Aiming at the problem of data sparsity, this paper clusters similar users through user information features, which provides an effective and reliable calculation range for the statistical average of the subsequent item scoring data. Then the statistical average of the item score data in the cluster is replaced by the defect value. Finally, the experimental results show that this method is effective in solving the problem of data sparsity. The experimental data set in this paper uses MovieLens-ml-100k, which includes the training set and the test set, etc. Finally, the algorithm proposed in this paper is experimentally analyzed by using the data set, and the correctness and superiority of the proposed algorithm are verified.
【學(xué)位授予單位】：昆明理工大學(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 周海平;黃湊英;劉妮;周洪波;;基于評(píng)分預(yù)測(cè)的協(xié)同過(guò)濾推薦算法[J];數(shù)據(jù)采集與處理;2016年06期

2 馬胡雙;石永革;高勝保;;基于特征增益與多級(jí)優(yōu)化的協(xié)同過(guò)濾個(gè)性化推薦算法[J];科學(xué)技術(shù)與工程;2016年21期

3 王瑞琴;蔣云良;李一嘯;樓俊鋼;;一種基于多元社交信任的協(xié)同過(guò)濾推薦算法[J];計(jì)算機(jī)研究與發(fā)展;2016年06期

4 周明建;趙建波;李騰;;基于情境相似的知識(shí)個(gè)性化推薦系統(tǒng)研究[J];計(jì)算機(jī)工程與科學(xué);2016年03期

5 楊武;唐瑞;盧玲;;基于內(nèi)容的推薦與協(xié)同過(guò)濾融合的新聞推薦方法[J];計(jì)算機(jī)應(yīng)用;2016年02期

6 吳毅濤;張興明;王興茂;李晗;;基于用戶模糊相似度的協(xié)同過(guò)濾算法[J];通信學(xué)報(bào);2016年01期

7 王夢(mèng)恬;魏晶晶;廖祥文;林錦賢;陳國(guó)龍;;融合評(píng)論標(biāo)簽的個(gè)性化推薦算法[J];計(jì)算機(jī)科學(xué)與探索;2016年10期

8 葉錫君;龔s，

本文編號(hào)：2239506