【摘要】：隨著信息技術(shù)和網(wǎng)絡(luò)技術(shù)的迅速發(fā)展,人們獲取信息的方式越來越多,但網(wǎng)絡(luò)中信息的爆炸式增長(zhǎng)使用戶迷失在信息的海洋中,越來越難準(zhǔn)確地檢索出自己真正需要的信息,即出現(xiàn)了信息超載現(xiàn)象。為了解決該問題,個(gè)性化推薦系統(tǒng)應(yīng)運(yùn)而生,它不需要用戶主動(dòng)輸入任何信息,通過分析用戶的歷史行為構(gòu)建用戶興趣模型,從而主動(dòng)向用戶推薦其可能感興趣的信息。個(gè)性化推薦系統(tǒng)的核心是其所采用的推薦算法,在眾多推薦算法中,協(xié)同過濾推薦算法是研究最多、應(yīng)用最廣的推薦算法。本文詳細(xì)分析了協(xié)同過濾推薦算法的工作流程,并針對(duì)數(shù)據(jù)稀疏問題和興趣遷移問題提出了一種改進(jìn)的協(xié)同過濾推薦算法來提高推薦系統(tǒng)的推薦質(zhì)量。本文的主要研究工作：(1)針對(duì)評(píng)分矩陣數(shù)據(jù)稀疏性問題,本文提出一種改進(jìn)的協(xié)同過濾算法,首先根據(jù)項(xiàng)目屬性對(duì)整個(gè)項(xiàng)目集進(jìn)行聚類,然后在每個(gè)聚類中用Slope One算法進(jìn)行填充,計(jì)算用戶相似性時(shí)采用的是用戶在每個(gè)聚類上的加權(quán)相似性。(2)傳統(tǒng)協(xié)同過濾推薦算法依靠評(píng)分?jǐn)?shù)據(jù)進(jìn)行推薦,沒有考慮到用戶興趣隨時(shí)間改變這一因素,越早的評(píng)分利用價(jià)值越低,為了更準(zhǔn)確地預(yù)測(cè)評(píng)分,本文將艾賓浩斯遺忘規(guī)律引入到推薦過程中,通過為每個(gè)評(píng)分增加一個(gè)時(shí)間權(quán)重來提高推薦系統(tǒng)的推薦質(zhì)量。(3)為了降低最近鄰居中極少數(shù)用戶對(duì)目標(biāo)項(xiàng)目評(píng)分的影響,先利用目標(biāo)用戶與最近鄰居中每個(gè)用戶的相似性對(duì)目標(biāo)項(xiàng)目進(jìn)行預(yù)測(cè)評(píng)分,得到一個(gè)虛擬最近鄰矩陣,然后在虛擬最近鄰矩陣中再次利用相似性進(jìn)行預(yù)測(cè)。最后,為了驗(yàn)證本文提出的改進(jìn)算法的有效性,采用MovieLens數(shù)據(jù)集分別對(duì)傳統(tǒng)協(xié)同過濾推薦算法和本文提出的改進(jìn)算法進(jìn)行試驗(yàn)比較,實(shí)驗(yàn)結(jié)果證明本文提出的改進(jìn)算法效果更佳。
[Abstract]:With the rapid development of information technology and network technology, there are more and more ways for people to obtain information. However, the explosive growth of information in the network makes users lost in the ocean of information, and it is more and more difficult to accurately retrieve the information they really need. That is, the phenomenon of information overload. In order to solve this problem, personalized recommendation system emerges as the times require, it does not need users to input any information actively, through analyzing the historical behavior of users to build user interest model, thus actively recommend the information that users may be interested in. The core of personalized recommendation system is its recommendation algorithm. Among many recommendation algorithms, collaborative filtering recommendation algorithm is the most widely studied and widely used recommendation algorithm. In this paper, the workflow of collaborative filtering recommendation algorithm is analyzed in detail, and an improved collaborative filtering recommendation algorithm is proposed to improve the recommendation quality of recommendation system. The main work of this paper is as follows: (1) aiming at the sparsity of scoring matrix data, an improved collaborative filtering algorithm is proposed. Firstly, the whole itemset is clustered according to the item attributes. Then, Slope one algorithm is used to fill each cluster, and the weighted similarity of users on each cluster is used to calculate the user similarity. (2) the traditional collaborative filtering recommendation algorithm relies on the score data to recommend. Not taking into account the change of user interest over time, the earlier the score is used, the lower the value. In order to predict the score more accurately, this paper introduces the rule of Ibinhaos forgetting into the recommendation process. By adding a time weight to each score to improve the recommendation quality of the recommendation system. (3) in order to reduce the impact of a very small number of users in the nearest neighbor on the target item score, A virtual nearest neighbor matrix is obtained by using the similarity between the target user and each user in the nearest neighbor, and then the similarity is used to predict the target item again. Finally, in order to verify the effectiveness of the proposed improved algorithm, MovieLens dataset is used to compare the traditional collaborative filtering recommendation algorithm with the improved algorithm proposed in this paper. The experimental results show that the improved algorithm proposed in this paper is more effective.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP391.3

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