本文關鍵詞： 協(xié)同過濾 多維相似度 模糊聚類 云模型　出處：《華中科技大學》2016年碩士論文　論文類型：學位論文

【摘要】：隨著互聯(lián)網(wǎng)技術的快速發(fā)展,數(shù)據(jù)呈現(xiàn)爆炸式增長,信息過載問題越來越引人注目。協(xié)同過濾推薦技術在解決信息過載問題方面已經(jīng)取得了不錯的效果,但在實際應用中隨著用戶和項目的增多,數(shù)據(jù)稀疏性和擴展性等問題仍然制約了算法的性能,這些問題成為該領域的研究熱點問題,具有很好的研究價值。因此,如何有效緩解基于協(xié)同過濾算法推薦系統(tǒng)中的數(shù)據(jù)稀疏性等問題、進一步提高推薦系統(tǒng)的預測準確度是本課題研究的主要目標。聚類技術常用于推薦系統(tǒng)中對用戶進行聚類,挖掘用戶的相似群體,進而有效的尋找合理的相似近鄰集合,從而提高預測準確度。因此,針對傳統(tǒng)Fuzzy C-Means算法對初始點敏感,易陷入局部最優(yōu)解的缺陷進行了改進,提出了一種改進的模糊聚類算法(SoMKfcm算法)。首先,提出了一種初始聚類中心選擇策略,有效避免噪音數(shù)據(jù)點的影響;其次,目標函數(shù)結合了樣本加權和樣本聚類中心距離,增加樣本屬性的非均衡性;最終對迭代求解過程進行優(yōu)化,結合了模擬退火算法,加入了求解的隨機跳躍性,避免結果陷入局部最優(yōu)解。在MATLAB平臺基于真實數(shù)據(jù)集上實驗結果表明,與傳統(tǒng)的算法相比,SoMKfcm算法具有更好的聚類效果和較好聚類準確度,并有效的改善傳統(tǒng)算法的缺陷。在上述工作基礎上,基于評分數(shù)據(jù)和用戶個人信息數(shù)據(jù),提出了一種結合云模型和用戶特征聚類的推薦算法(CCCF算法)。首先,利用用戶個人信息和云模型逆向云算法來重構評分數(shù)據(jù),生成用戶融合行為偏好向量。其次,在融合行為偏好矩陣的基礎上利用SoMKfcm方法對用戶進行模糊聚類,給出了重要性群體選擇策略,為后續(xù)步驟提供數(shù)據(jù)平滑和近鄰用戶集的選擇,進而提出了一種多維相似度計算方法。最后,基于上述結果進行評分預測。為了驗證CCCF推薦算法的有效性,本文在Moveilens 1m和Moveilens 100k數(shù)據(jù)集上與其他幾種相關算法進行對比實驗。實驗結果表明:在不同稀疏度情況下,CCCF算法能夠有效緩解數(shù)據(jù)稀疏性對推薦算法的影響,算法預測準確度得到明顯提高。
[Abstract]:With the rapid development of Internet technology, data showing explosive growth, the problem of information overload is becoming more and more noticeable. The collaborative filtering technology in solving the problem of information overload has achieved good results, but in the actual application, with the increase of users and items, data sparsity and scalability problems still restrict the performance of the algorithm. These problems have become the hot issues in the field, it has great research value. Therefore, how to effectively ease the collaborative filtering recommendation system based on the data sparseness problem, further improve the prediction accuracy of the recommendation system is the main goal of this research. Clustering techniques are commonly used in Recommendation System for users clustering. A similar group of mining user, thus effectively find reasonable similar neighbor sets, so as to improve the prediction accuracy. Therefore, the traditional Fuzzy C -Means algorithm is sensitive to the initial point and easy to fall into the local optimal solution of the defects are improved, the paper puts forward an improved fuzzy clustering algorithm (SoMKfcm algorithm) is proposed. Firstly, an initial clustering center selection strategy, effectively avoid the effect of noise data points; secondly, the function of target distance weighted sample and sample the clustering center, increase non balanced sample attribute; the final of the iterative process is optimized, combined with simulated annealing algorithm, adds a random jump for the results to avoid falling into local optimal solution. In the MATLAB platform based on real data sets. The experimental results show that compared with the traditional algorithm, SoMKfcm algorithm has better clustering effect and better clustering accuracy, and improve the traditional algorithm defects. Based on the above work, the score data and the user's personal information based on the data, proposes a combination of cloud Recommendation algorithm and user clustering model (CCCF algorithm). Firstly, the personal information of the user and cloud model using reverse cloud algorithm to reconstruct the score data fusion to generate user behavior preference vector. Secondly, based on the fusion behavior preference matrix using SoMKfcm method for users of fuzzy clustering, the importance of group selection strategy is given. Provide data smoothing and neighbor users set selection for subsequent steps, and then proposes a multidimensional similarity calculation method. Finally, based on the results of rating prediction. In order to verify the validity of the CCCF recommendation algorithm, this paper is related to several other Moveilens 1m and Moveilens 100k data sets algorithm. The results of experiments. Under different sparsity conditions, CCCF algorithm can effectively alleviate the influence of data sparsity recommendation algorithm, algorithm prediction accuracy is significantly improved.

【學位授予單位】：華中科技大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TP391.3

【參考文獻】

相關期刊論文 前4條

1 張峻瑋;楊洲;;一種基于改進的層次聚類的協(xié)同過濾用戶推薦算法研究[J];計算機科學;2014年12期

2 吳湖;王永吉;王哲;王秀利;杜栓柱;;兩階段聯(lián)合聚類協(xié)同過濾算法[J];軟件學報;2010年05期

3 李政偉;譚國俊;;改進的退火遺傳優(yōu)化策略應用研究[J];計算機工程與應用;2010年04期

4 張光衛(wèi);李德毅;李鵬;康建初;陳桂生;;基于云模型的協(xié)同過濾推薦算法[J];軟件學報;2007年10期

，

本文編號：1451836