本文關(guān)鍵詞：基于協(xié)同過濾和加權(quán)二部圖的推薦算法研究　出處：《吉林大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 協(xié)同過濾 數(shù)據(jù)預(yù)處理 層次聚類 kmeans++ 加權(quán)二部圖 預(yù)測(cè)評(píng)分

【摘要】：隨著互聯(lián)網(wǎng)數(shù)據(jù)的急劇增長(zhǎng),信息過載問題日益嚴(yán)重,推薦是解決信息過載的主要方式之一。然而傳統(tǒng)的協(xié)同過濾推薦算法存在推薦效率不高,準(zhǔn)確率低,內(nèi)存溢出等問題;另外隨著數(shù)據(jù)量的增加數(shù)據(jù)稀疏越來越嚴(yán)重,傳統(tǒng)協(xié)同過濾推薦結(jié)果越發(fā)不精確。為了解決上述問題,提高推薦算法的效率和精度,本文做了一定的改進(jìn),具體工作包括以下幾個(gè)方面。首先,對(duì)數(shù)據(jù)預(yù)處理方法進(jìn)行了改進(jìn),使用時(shí)間衰減曲線可以更加契合用戶的興趣隨著時(shí)間變化這一事實(shí),更加逼近用戶真實(shí)興趣;采用高斯規(guī)范化處理原始數(shù)據(jù),去除了由于個(gè)人因素造成的評(píng)分標(biāo)準(zhǔn)不統(tǒng)一的問題,預(yù)處理后數(shù)據(jù)更加的規(guī)范和標(biāo)準(zhǔn)。其次,使用改進(jìn)的層次聚類算法和Kmeans算法對(duì)大量的用戶劃分聚類,將用戶劃分為若干個(gè)相似的用戶類。該方法對(duì)聚類距離重新定義,既考慮共同評(píng)分項(xiàng)對(duì)用戶相似度的影響,又兼顧公眾項(xiàng)及用戶評(píng)分重合度的影響,使得聚類結(jié)果更加符合真實(shí)情況,同時(shí)也對(duì)用戶興趣社區(qū)發(fā)現(xiàn)奠定了基礎(chǔ)。最后,在聚類的基礎(chǔ)上對(duì)每個(gè)用戶子類和對(duì)應(yīng)的項(xiàng)目子類重新建模,用二部圖的思想對(duì)未評(píng)分項(xiàng)進(jìn)行預(yù)測(cè)評(píng)分。本文在二部圖算法基礎(chǔ)上進(jìn)行了一定的改進(jìn),提出加權(quán)二部圖算法,該算法明顯降低了矩陣運(yùn)算復(fù)雜度,提高了預(yù)測(cè)精準(zhǔn)度,明顯提高了運(yùn)算推薦效率,為以后做實(shí)時(shí)推薦提供了理論基礎(chǔ)。經(jīng)過以上三個(gè)方面的改進(jìn),本文提出了基于加權(quán)二部圖及劃分聚類相結(jié)合的協(xié)同過濾推薦算法。該算法能夠較好的處理傳統(tǒng)算法的不足,緩解了數(shù)據(jù)稀疏、推薦準(zhǔn)確率較低、內(nèi)存溢出等問題,同時(shí)提高了算法的效率和精度。最后使用Movielens數(shù)據(jù)集,通過具體的編碼試驗(yàn)驗(yàn)證,結(jié)果證明,優(yōu)化后的算法在MAE、RMSE、推薦準(zhǔn)確度以及算法效率等方面都得到了較好的結(jié)果。
[Abstract]:With the rapid growth of Internet data, the growing problem of information overload, recommendation is one of the main ways to solve the problem of information overload. However, traditional collaborative filtering recommendation algorithm has recommended the efficiency is not high, the rate of accuracy is low, the memory overflow problem; also with the increased amount of data sparse data more and more serious, the traditional collaborative filtering recommendation results more not accurate. In order to solve the above problems, improve the efficiency and accuracy of the algorithm, this paper made some improvements, the specific work includes the following aspects. Firstly, the data preprocessing method was improved. The attenuation curve can be more fit the user's interest with the fact that the time changes, more close to the real user interest; Gauss used standardized processing of raw data, removal of the standard for evaluation due to personal factors are not uniform, after preprocessing the data more Norms and standards. Secondly, the user clustering is the use of a large number of improved hierarchical clustering algorithm and Kmeans algorithm, the user will be divided into a number of similar users. The method re definition of clustering distance, both common rating items affect user similarity, but also affect the public and user rating of coincidence degree. Makes the clustering results more in line with the real situation, but also found that laid the foundation of interest to the user community. Finally, users and the corresponding items for each sub class re modeling based on the cluster, predict the score of rating items with two figure of thought. This paper makes some improvements in the two figure based on the proposed algorithm, two weighted graph algorithm, this algorithm significantly reduces the computational complexity of matrix, improve the prediction accuracy, significantly improves the operation efficiency for the real-time recommendation, Recommendation provides The theoretical basis. Through the improvement of the above three aspects, this paper proposes a collaborative filtering recommendation algorithm based on weighted two graph and clustering based on combination. The algorithm can lack the traditional algorithm better, alleviate the data sparsity, the recommendation accuracy is low, the memory overflow problem, and improve the efficiency and precision of the algorithm using the Movielens data set. Finally, through the encoding test, the results show that the optimized algorithm in MAE, RMSE, recommendation accuracy and efficiency of the algorithm and the reasonable results are obtained.

【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.3

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