基于用戶聚類(lèi)的協(xié)同過(guò)濾推薦算法研究
發(fā)布時(shí)間：2019-03-07 22:18
【摘要】：隨著互聯(lián)網(wǎng)的普及,大量無(wú)意義的數(shù)據(jù)給人們篩選有效信息帶來(lái)巨大的困難。為了幫助人們快速有效的篩選信息,個(gè)性化推薦系統(tǒng)應(yīng)運(yùn)而生。推薦算法作為推薦系統(tǒng)的核心,一直是研究的重點(diǎn)。在眾多的推薦算法中,協(xié)同過(guò)濾算法是應(yīng)用最廣泛的。協(xié)同過(guò)濾算法通過(guò)對(duì)用戶歷史行為數(shù)據(jù)的挖掘發(fā)現(xiàn)用戶的偏好,從而基于不同的偏好對(duì)用戶進(jìn)行群組劃分并推薦品味相似的物品。然而,隨著電子商務(wù)系統(tǒng)中用戶數(shù)和項(xiàng)目數(shù)的不斷增大,數(shù)據(jù)的稀疏性和推薦效率逐漸成為制約協(xié)同過(guò)濾算法發(fā)展的瓶頸。為了提高協(xié)同過(guò)濾算法的推薦質(zhì)量和推薦效率,本文提出一種基于改進(jìn)的用戶聚類(lèi)協(xié)同過(guò)濾推薦算法,并基于改進(jìn)算法設(shè)計(jì)和實(shí)現(xiàn)了一個(gè)B/S架構(gòu)的電影推薦系統(tǒng)。本文介紹了個(gè)性化推薦系統(tǒng)的發(fā)展背景和架構(gòu)設(shè)計(jì),給出了傳統(tǒng)協(xié)同過(guò)濾算法的基本思想和面臨的主要問(wèn)題,從而從離線用戶聚類(lèi)和用戶相似度計(jì)算兩個(gè)方面改進(jìn)了傳統(tǒng)算法。對(duì)算法的改進(jìn)主要體現(xiàn)在兩個(gè)方面:一是綜合考慮了用戶評(píng)分信息和項(xiàng)目類(lèi)別偏好信息對(duì)用戶聚類(lèi)的影響,提出一種聯(lián)合用戶聚類(lèi)算法。該算法分別基于用戶評(píng)分信息和項(xiàng)目類(lèi)別偏好信息對(duì)基本用戶聚類(lèi),產(chǎn)生兩個(gè)聚類(lèi)中心和兩個(gè)用戶類(lèi)別所屬矩陣,計(jì)算目標(biāo)用戶與兩個(gè)聚類(lèi)中心的相似度以及目標(biāo)用戶在不同聚類(lèi)中所屬的類(lèi)簇,對(duì)結(jié)果合并去重后得到目標(biāo)用戶的最近鄰居搜索空間。二是針對(duì)傳統(tǒng)Pearson相關(guān)系數(shù)計(jì)算相似度時(shí)對(duì)絕對(duì)數(shù)值不敏感等問(wèn)題,提出一種基于差異因子的加權(quán)Pearson相關(guān)系數(shù)計(jì)算方法,將評(píng)分差異因子作為權(quán)重來(lái)修正傳統(tǒng)的Pearson相關(guān)系數(shù)。采用MovieLens數(shù)據(jù)集,以MAE值、準(zhǔn)確率、召回率和F1值為度量標(biāo)準(zhǔn),通過(guò)多組實(shí)驗(yàn)對(duì)改進(jìn)算法、傳統(tǒng)基于用戶的協(xié)同過(guò)濾算法(CF)、傳統(tǒng)基于用戶聚類(lèi)的協(xié)同過(guò)濾算法(UCCF)進(jìn)行評(píng)估,實(shí)驗(yàn)結(jié)果表明改進(jìn)算法能夠有效提高推薦系統(tǒng)的推薦效率和推薦精度。本文基于改進(jìn)算法設(shè)計(jì)并實(shí)現(xiàn)了電影推薦系統(tǒng),系統(tǒng)采用豆瓣Top250電影信息作為數(shù)據(jù)集,使用PHP和Matlab混合編程實(shí)現(xiàn),能夠根據(jù)用戶的偏好信息為用戶提供個(gè)性化的推薦服務(wù)。
[Abstract]:With the popularity of the Internet, a large number of meaningless data for people to screen effective information has brought great difficulties. In order to help people to screen information quickly and effectively, personalized recommendation system emerges as the times require. As the core of recommendation system, recommendation algorithm has always been the focus of research. Among the many recommendation algorithms, collaborative filtering algorithm is the most widely used. The collaborative filtering algorithm discovers the user's preference by mining the user's historical behavior data, and then groups the users based on different preferences and recommends the items with similar taste. However, with the increasing number of users and items in e-commerce system, the sparsity of data and the efficiency of recommendation gradually become the bottleneck to restrict the development of collaborative filtering algorithm. In order to improve the recommendation quality and efficiency of collaborative filtering algorithm, this paper proposes an improved collaborative filtering recommendation algorithm based on user clustering, and designs and implements a movie recommendation system based on the improved algorithm. This paper introduces the development background and architecture design of personalized recommendation system, gives the basic idea and main problems of traditional collaborative filtering algorithm, and improves the traditional algorithm from two aspects: off-line user clustering and user similarity calculation. The improvement of the algorithm is mainly reflected in two aspects: first, considering the influence of user rating information and item class preference information on user clustering, a joint user clustering algorithm is proposed. Based on the user rating information and item class preference information, the algorithm clusters the basic users respectively, and generates two clustering centers and two user categories belong to the matrix. The similarity between the target user and the two clustering centers and the cluster belonging to the target user in different clusters are calculated. The nearest neighbor search space of the target user is obtained after the result is merged and deduplicated. Secondly, aiming at the problem that the traditional Pearson correlation coefficient is insensitive to absolute value when calculating the similarity degree, a weighted Pearson correlation coefficient calculation method based on the difference factor is proposed, in which the score difference factor is used as the weight to correct the traditional Pearson correlation coefficient. Using MovieLens data set and mae value, accuracy rate, recall rate and F1 value as metrics, the improved algorithm is improved by multi-group experiments, and the traditional user-based collaborative filtering algorithm (CF), is used. The traditional collaborative filtering algorithm based on user clustering (UCCF) is evaluated. The experimental results show that the improved algorithm can effectively improve the recommendation efficiency and accuracy of the recommendation system. In this paper, a movie recommendation system is designed and implemented based on the improved algorithm. The system uses Douban Top250 movie information as data set and mixed programming with PHP and Matlab, which can provide personalized recommendation service to users according to their preference information.
【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】：2017
【分類(lèi)號(hào)】：TP391.3
本文編號(hào)：2436505
[Abstract]:With the popularity of the Internet, a large number of meaningless data for people to screen effective information has brought great difficulties. In order to help people to screen information quickly and effectively, personalized recommendation system emerges as the times require. As the core of recommendation system, recommendation algorithm has always been the focus of research. Among the many recommendation algorithms, collaborative filtering algorithm is the most widely used. The collaborative filtering algorithm discovers the user's preference by mining the user's historical behavior data, and then groups the users based on different preferences and recommends the items with similar taste. However, with the increasing number of users and items in e-commerce system, the sparsity of data and the efficiency of recommendation gradually become the bottleneck to restrict the development of collaborative filtering algorithm. In order to improve the recommendation quality and efficiency of collaborative filtering algorithm, this paper proposes an improved collaborative filtering recommendation algorithm based on user clustering, and designs and implements a movie recommendation system based on the improved algorithm. This paper introduces the development background and architecture design of personalized recommendation system, gives the basic idea and main problems of traditional collaborative filtering algorithm, and improves the traditional algorithm from two aspects: off-line user clustering and user similarity calculation. The improvement of the algorithm is mainly reflected in two aspects: first, considering the influence of user rating information and item class preference information on user clustering, a joint user clustering algorithm is proposed. Based on the user rating information and item class preference information, the algorithm clusters the basic users respectively, and generates two clustering centers and two user categories belong to the matrix. The similarity between the target user and the two clustering centers and the cluster belonging to the target user in different clusters are calculated. The nearest neighbor search space of the target user is obtained after the result is merged and deduplicated. Secondly, aiming at the problem that the traditional Pearson correlation coefficient is insensitive to absolute value when calculating the similarity degree, a weighted Pearson correlation coefficient calculation method based on the difference factor is proposed, in which the score difference factor is used as the weight to correct the traditional Pearson correlation coefficient. Using MovieLens data set and mae value, accuracy rate, recall rate and F1 value as metrics, the improved algorithm is improved by multi-group experiments, and the traditional user-based collaborative filtering algorithm (CF), is used. The traditional collaborative filtering algorithm based on user clustering (UCCF) is evaluated. The experimental results show that the improved algorithm can effectively improve the recommendation efficiency and accuracy of the recommendation system. In this paper, a movie recommendation system is designed and implemented based on the improved algorithm. The system uses Douban Top250 movie information as data set and mixed programming with PHP and Matlab, which can provide personalized recommendation service to users according to their preference information.
【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】：2017
【分類(lèi)號(hào)】：TP391.3
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