發(fā)布時間：2018-05-22 15:18

  本文選題：推薦系統(tǒng) + 聚類　； 參考：《河北大學(xué)》2017年碩士論文

【摘要】：進入21世紀以來,在互聯(lián)網(wǎng)和電子商務(wù)網(wǎng)站飛速發(fā)展的背景下,電子商務(wù)網(wǎng)站中的信息量變得更為龐大和復(fù)雜,繁冗的數(shù)據(jù)給電子商務(wù)的發(fā)展帶來巨大的挑戰(zhàn)。為了解決這一難題,針對電子商務(wù)的推薦系統(tǒng)應(yīng)運而生,電子商務(wù)推薦系統(tǒng)主要目的是幫助用戶迅速的定位到自己喜歡的商品。在目前主流的各種推薦算法中,協(xié)同過濾算法是一種應(yīng)用較廣的推薦算法,但傳統(tǒng)的協(xié)同過濾存在“稀疏性”、“冷啟動”和“可擴展性”等問題。近年來關(guān)于推薦系統(tǒng)的研究中,一些學(xué)者提出將聚類技術(shù)引入到推薦系統(tǒng)中用以解決上述問題。基于聚類的推薦算法通過先對用戶或者項目進行聚類劃分,使得相似度較高的對象聚集到同一個類中,從而簡化查找最近鄰居的過程,大大減小了整體計算復(fù)雜度和時間消耗。另外由于聚類過程可以在線下完成,所以大大提升了推薦系統(tǒng)整體的實時性。本文提出了基于AP聚類的推薦算法,主要研究內(nèi)容如下:(1)提出并設(shè)計了基于AP聚類的推薦算法。將AP聚類算法引入到推薦系統(tǒng)的用戶分類過程中,僅需要將目標用戶通過AP聚類方法進行分類,簡化查找最近鄰居和計算對象相似度的過程,降低了在整體計算中的復(fù)雜度和時間消耗。(2)傳統(tǒng)的AP聚類不包括類別的合并過程,使得聚類的精度較差,尤其是對結(jié)構(gòu)復(fù)雜的數(shù)據(jù)。本文提出了一種基于屬性加權(quán)的度量方法,基于此對AP聚類算法進行了改進。(3)設(shè)計并實現(xiàn)了基于改進AP聚類的推薦算法。在公共數(shù)據(jù)集上進行了仿真實驗,評價指標為平均絕對偏差(MAE)值,芮氏指標(RI)和純度指標(Purity),實驗結(jié)果表明了本文算法的有效性。
[Abstract]:Since the beginning of the 21st century, with the rapid development of the Internet and e-commerce websites, the amount of information in e-commerce websites has become larger and more complex, and the redundant data has brought great challenges to the development of e-commerce. In order to solve this problem, E-commerce recommendation system emerges as the times require. The main purpose of E-commerce recommendation system is to help users locate their favorite products quickly. Collaborative filtering is one of the most popular recommendation algorithms, but there are some problems in the traditional collaborative filtering, such as "sparsity", "cold start" and "expansibility". In recent years, in the research of recommendation system, some scholars have proposed to introduce clustering technology to the recommendation system to solve the above problems. The recommendation algorithm based on clustering makes objects with high similarity gather into the same class by clustering users or items, thus simplifying the process of finding nearest neighbors and greatly reducing the overall computational complexity and time consumption. In addition, the clustering process can be completed off-line, so it greatly improves the real-time performance of the recommendation system as a whole. This paper proposes a recommendation algorithm based on AP clustering. The main research contents are as follows: 1) A recommendation algorithm based on AP clustering is proposed and designed. The AP clustering algorithm is introduced into the user classification process of recommendation system. It is only necessary to classify the target users by AP clustering method to simplify the process of finding nearest neighbor and computing object similarity. It reduces the complexity and time consumption in the whole computation.) the traditional AP clustering does not include the merging process of the categories, which makes the accuracy of the clustering worse, especially for the data with complex structure. In this paper, an attribute weighted measurement method is proposed. Based on this, an improved AP clustering algorithm is designed and a recommendation algorithm based on improved AP clustering is implemented. Simulation experiments are carried out on common data sets. The evaluation indexes are mean absolute deviation (mae), Rui's index and purity index. The experimental results show the effectiveness of the proposed algorithm.
【學(xué)位授予單位】：河北大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.3

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本文編號：1922609