本文關(guān)鍵詞： 推薦算法 協(xié)同過濾 項目相似度學(xué)習(xí) 社交網(wǎng)絡(luò) 標簽　出處：《安徽大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著互聯(lián)網(wǎng)的發(fā)展,推薦算法已經(jīng)應(yīng)用到很多領(lǐng)域,協(xié)同過濾推薦算法是經(jīng)典的、應(yīng)用廣泛的推薦算法。然而傳統(tǒng)的協(xié)同過濾推薦算法面臨著很多問題,其中最嚴重的是冷啟動問題、數(shù)據(jù)稀疏問題和擴展性問題。本文針對這些問題,對傳統(tǒng)的協(xié)同過濾推薦算法做了一定的改進。首先,針對數(shù)據(jù)稀疏性問題,本文提出了一種基于項目相似度學(xué)習(xí)的協(xié)同過濾推薦算法。該算法首先根據(jù)項目屬性相似性度量方法計算出所有項目的相似度矩陣,然后選取目標項目的前K個最相似的項目作為其初始鄰近集;再將訓(xùn)練集中目標項目的評分向量作為期望輸出,目標項目的K個鄰近項目的評分向量輸入到RBF神經(jīng)網(wǎng)絡(luò)中進行學(xué)習(xí),得到項目相似度訓(xùn)練模型;再將測試數(shù)據(jù)集中的目標項目的K個鄰近項目的評分向量輸入訓(xùn)練模型,最后輸出目標項目的預(yù)測評分向量。針對新項目冷啟動問題,我們計算出新加入項目與其他項目的屬性相似度,然后取出前K個最相似的項目構(gòu)成鄰近集并且計算出新加入項目的預(yù)測評分向量。最后取出對目標項目評分大于等于3且分數(shù)排在前N位的用戶,并將目標項目推薦給這些用戶。其次,針對數(shù)據(jù)稀疏性和擴展性問題,本文提出了一種基于社交網(wǎng)絡(luò)和標簽的協(xié)同過濾推薦算法。該算法將目標用戶與他的朋友之間的信任度、熟悉度和標簽信息反映的興趣偏好相似度結(jié)合起來,計算出與他相似度較高的K個朋友作為鄰居集合,從而為目標用戶推薦喜歡的項目;然后,針對新用戶冷啟動問題,提出了基于樸素貝葉斯算法的模型。它利用樸素貝葉斯算法對訓(xùn)練集中的用戶進行分類,將新用戶劃分到所屬的類別,即求出新用戶最喜歡的項目類型,然后在這種類型的項目里選擇評分最高的N個項目推薦給該用戶。最后,在Movielens數(shù)據(jù)集上實現(xiàn)基于項目相似度學(xué)習(xí)的協(xié)同過濾推薦算法,交叉實驗表明,該算法在處理稀疏數(shù)據(jù)時表現(xiàn)出了較好的性能,并且得到了更準確的推薦結(jié)果;在Last.fm數(shù)據(jù)集上實現(xiàn)基于社交網(wǎng)絡(luò)和標簽的協(xié)同過濾推薦算法,與傳統(tǒng)的算法和一些經(jīng)典的算法相比,該算法具有較好的準確性和高效性。最后,在Movielens數(shù)據(jù)集上驗證了項目冷啟動和用戶冷啟動問題,實驗表明算法在一定程度上解決了冷啟動問題。
[Abstract]:With the development of the Internet, recommendation algorithms have been applied to many fields. Collaborative filtering recommendation algorithms are classic and widely used. However, the traditional collaborative filtering recommendation algorithms face many problems. The most serious problems are cold start problem, data sparse problem and expansibility problem. In this paper, some improvements are made to the traditional collaborative filtering recommendation algorithm. In this paper, a collaborative filtering recommendation algorithm based on item similarity learning is proposed. Then the first K similar items of the target item are selected as its initial adjacent set, and the score vector of the target item in the training set is taken as the expected output. The score vectors of K adjacent items of the target items are input into the RBF neural network for learning, and the item similarity training model is obtained, and then the score vectors of K adjacent items in the test data set are input into the training model. Finally, we output the prediction score vector of the target item. For the cold start problem of the new project, we calculate the attribute similarity between the new item and other items. Then take out the first K most similar items to form the adjacent set and calculate the predicted score vector for the new item. Finally, take out the user whose target item score is greater than or equal to 3 and scores in the top N position. Secondly, a collaborative filtering recommendation algorithm based on social networks and tags is proposed to solve the problem of data sparsity and scalability, which brings forward the trust between the target user and his friend. By combining familiarity with interest preference similarity reflected by label information, K friends with high similarity are calculated as neighbors to recommend favorite items for target users. This paper presents a model based on naive Bayes algorithm, which classifies users in training set and divides new users into categories, that is, to find out the type of items that new users like most. Finally, a collaborative filtering recommendation algorithm based on item similarity learning is implemented on the Movielens dataset. The algorithm shows better performance in dealing with sparse data and gets more accurate recommendation results, and implements collaborative filtering recommendation algorithm based on social networks and tags on Last.fm datasets. Compared with the traditional algorithm and some classical algorithms, this algorithm has better accuracy and high efficiency. Finally, the cold start problem of the project and the cold start problem of the user are verified on the Movielens data set. Experiments show that the algorithm solves the cold start problem to some extent.
【學(xué)位授予單位】：安徽大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.3

【參考文獻】

相關(guān)期刊論文 前10條

1 賀懷清;范志亮;劉浩翰;;基于網(wǎng)絡(luò)社區(qū)劃分的協(xié)同推薦算法[J];中國民航大學(xué)學(xué)報;2016年05期

2 時念云;葛曉偉;馬力;;基于用戶人口統(tǒng)計特征與信任機制的協(xié)同推薦[J];計算機工程;2016年06期

3 王升升;趙海燕;陳慶奎;曹健;;個性化推薦中的隱語義模型[J];小型微型計算機系統(tǒng);2016年05期

4 王升升;趙海燕;陳慶奎;曹健;;基于社交標簽和社交信任的概率矩陣分解推薦算法[J];小型微型計算機系統(tǒng);2016年05期

5 趙文濤;王春春;成亞飛;孟令軍;趙好好;;基于用戶多屬性與興趣的協(xié)同過濾算法[J];計算機應(yīng)用研究;2016年12期

6 韓亞楠;曹菡;劉亮亮;;基于評分矩陣填充與用戶興趣的協(xié)同過濾推薦算法[J];計算機工程;2016年01期

7 王夢恬;魏晶晶;廖祥文;林錦賢;陳國龍;;融合評論標簽的個性化推薦算法[J];計算機科學(xué)與探索;2016年10期

8 葉錫君;龔s，

本文編號：1547918