本文關鍵詞： 網(wǎng)絡購物 相似離度 加權 RA 鏈路預測 共同趨向性　出處：《首都經(jīng)濟貿(mào)易大學》2015年碩士論文　論文類型：學位論文

【摘要】：電子商務的迅猛發(fā)展,讓網(wǎng)絡購物成為一種趨勢,但身處大數(shù)據(jù)時代,面對海量的商品信息,很容易讓顧客的購物興趣減弱。推薦系統(tǒng)的出現(xiàn)在一定程度上解決了這個問題,而推薦算法在推薦系統(tǒng)中扮演著重要的角色。本文運用集合相似度理論、復雜網(wǎng)絡的相關理論及鏈路預測的知識,從顧客的角度出發(fā),利用歷史購買記錄對顧客關系進行分析,運用加權RA消除非常規(guī)顧客對相似性計算的影響。通過進行鏈路預測的方法,構建顧客共同趨向性獲取算法,進而基于相關顧客購物共同趨向性得到目標顧客最有可能購買的商品。通過將所建模型的求解結果與實驗驗證結果進行對比分析,得出本文算法的可行性。對于模型的構建主要運用以下方法:(1)集合運算。通過讓大學生作為顧客進行商品購買,從而得到顧客 商品集合,集合運算得到顧客 商品關系矩陣。(2)相似性算法。針對顧客是否為孤立點這兩種情況,綜合運用余弦相似系數(shù)和相對歐式距離系數(shù)進行顧客相似離度的求解,既考慮了樣本內(nèi)數(shù)據(jù)變化規(guī)律的差異也考慮了樣本數(shù)據(jù)的數(shù)值差異。為了消除主流顧客對顧客相似性計算的影響,在此基礎上將相似離度值作為權重進行加權RA的計算,運用pajek構建顧客相似關系網(wǎng)絡。(3)共同趨向性獲取算法。針對最相似顧客數(shù)量的不同分別進行推薦,基于相關顧客購物共同趨向性得到目標顧客最有可能購買的商品。
[Abstract]:With the rapid development of electronic commerce, online shopping has become a trend, but in the era of big data, it is easy to weaken the customer's interest in shopping in the face of massive commodity information. The appearance of recommendation system solves this problem to a certain extent. The recommendation algorithm plays an important role in the recommendation system. Using the theory of set similarity, the related theory of complex network and the knowledge of link prediction, from the customer's point of view, using the historical purchase record to analyze the customer relationship, this paper analyzes the relationship of customer by using the theory of set similarity, the theory of complex network and the knowledge of link prediction. The weighted RA is used to eliminate the influence of unconventional customers on similarity calculation. Through the method of link prediction, a customer common trend acquisition algorithm is constructed. Then, based on the common tendency of relevant customers, we get the most likely items to be purchased by the target customers. The results of the model are compared with the experimental results. The feasibility of this algorithm is obtained. The following method is mainly used to construct the model: 1) set operation. By making college students buy goods as customers, we can get the set of customers. The similarity algorithm of customer's merchandise relation matrix is obtained by set operation. In view of whether the customer is an outlier or not, the similarity degree of customer is solved by using cosine similarity coefficient and relative Euclidean distance coefficient synthetically. In order to eliminate the influence of mainstream customers on customer similarity calculation, the similarity deviation value is used as the weight to calculate the weighted RA in order to eliminate the influence of mainstream customers on customer similarity calculation. Using pajek to construct customer similarity relationship network. (3) Common trend acquisition algorithm. According to the different number of most similar customers, we recommend the most likely products for the target customers based on the common trend of customer shopping.
【學位授予單位】：首都經(jīng)濟貿(mào)易大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TP391.3;F724.6

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本文編號：1540380