本文關(guān)鍵詞：輕量級服務(wù)推薦算法研究　出處：《北京郵電大學(xué)》2015年博士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 協(xié)同過濾 服務(wù)推薦 基于比值的相似度 自組織網(wǎng)絡(luò) 上下文

【摘要】：信息技術(shù)的發(fā)展給人們的生活帶來了巨大的便利。隨著網(wǎng)絡(luò)中信息的大量增加,信息出現(xiàn)了過載現(xiàn)象。為了使用戶準確地獲取所需的信息,推薦系統(tǒng)應(yīng)運而生。因為能帶來巨大的商業(yè)價值和利益,無論是在學(xué)術(shù)界還是工業(yè)界,推薦系統(tǒng)都受到了極大的關(guān)注。在學(xué)術(shù)界,出現(xiàn)了許多高效的推薦方法,在工業(yè)界,推薦系統(tǒng)被廣泛應(yīng)用在各種場合。服務(wù)推薦是推薦系統(tǒng)的應(yīng)用實例之一。 目前,基于協(xié)同過濾的推薦是推薦系統(tǒng)中一種廣泛使用的算法。在基于評分的協(xié)同過濾推薦過程中,有兩個重要的科學(xué)問題：一個是用戶或者物品(item)之間的相似度計算,另一個是當前用戶對當前物品的評分值預(yù)測。現(xiàn)有相似度計算方法及評分值預(yù)測方法在準確度和效率等方面性能有待進一步提高。 本文主要研究基于協(xié)同過濾的推薦問題。從相似度計算、評分值預(yù)測以及自組織網(wǎng)絡(luò)中的推薦問題等方面,本文主要做了如下的研究工作： (1)：針對評分的推薦中相似度計算問題,提出了一種基于比值的相似度計算方法。通過比較用戶對共同評價過的物品的評分,即可得出用戶的相似度。通過比較相同的用戶對不同物品的評分即可得出物品的相似度。避免了目前許多相似度計算方法中復(fù)雜的運算。實驗結(jié)果表明,本文提出的相似度計算方法比文中的對比方法更為有效。 (2)：針對未知評分值預(yù)測問題,本文在提出的基于比值的相似度計算方法的基礎(chǔ)上,提出了一種新的未知評分值預(yù)測的方法。該方法只需簡單的運算并比較用戶評分的大小以及統(tǒng)計數(shù)量即可得到預(yù)測值。為了評價本文提出的方法的有效性,本文以真實的大規(guī)模web服務(wù)數(shù)據(jù)集為基礎(chǔ),與現(xiàn)有的幾種主要預(yù)測方法進行了對比。實驗結(jié)果顯示,本文提出的方法在誤差較小的預(yù)測值數(shù)量、平均絕對誤差(MAE)以及預(yù)測時間等方面優(yōu)于對比方法。 (3)：為了研究移動自組織網(wǎng)絡(luò)環(huán)境中非評分的服務(wù)推薦問題,本文提出了一種自組織網(wǎng)絡(luò)中非評分的服務(wù)推薦模型,提出了一種在自組織網(wǎng)絡(luò)中進行服務(wù)推薦的節(jié)點之間的相似度計算方法。本文認為節(jié)點之間的相似度包含兩方面的因素,一方面是移動終端的客觀性因素,另一方面是節(jié)點所在的用戶的主觀性因素。本文根據(jù)節(jié)點的上下文信息,提出了一種節(jié)點客觀性部分的相似度計算方法；同時,根據(jù)用戶的行為信息,提出了一種非評分的節(jié)點主觀性部分的相似度計算方法。最后通過實驗研究了自組織網(wǎng)絡(luò)中各種因素對服務(wù)推薦成功率的影響。
[Abstract]:The development of information technology has brought great convenience to people's life. With the increase of network information, information overload. In order to accurately obtain the required information, the recommendation system came into being. Because it can bring huge commercial value and interest, whether in academia or industry, recommended the system has attracted great attention in academic circles, there are many efficient methods recommended, in the industrial sector, the recommendation system is widely used in various occasions. Service recommendation is one of the application examples of the recommendation system.
At present, recommendation based on collaborative filtering is a widely used algorithm in recommendation system. Based on the scores of the collaborative filtering recommendation process, there are two important scientific problems: one is the user or item (item) between similarity computation, another is the current user rating items on the current value of the forecast. The existing similarity calculation method and score prediction method in terms of accuracy and efficiency need to be further improved.
In this paper, we mainly study collaborative filtering recommendation problem. From the aspects of similarity computation, score prediction and recommendation in self organizing network, we have done the following research work.
(1): according to the similarity score of the recommendation of the computational problems, put forward a calculation method based on the similarity ratio. Through the comparison of common user items evaluation score, can be obtained by comparing the similarity of users. The same user of different objects can be obtained. The similarity score items to avoid many similarity in the method of complex computation. Experimental results show that the similarity calculation method than in contrast method is more effective.
(2): for unknown score prediction problem, this paper puts forward the calculation method of the similarity ratio based on the proposed prediction method, a new unknown score. This method only needs simple operation and compared the size and number of users score statistics can get the forecast value. Effective method for this paper presents the evaluation, based on the large-scale web services real data sets as the basis, compared with several existing main prediction methods. Experimental results show that the proposed method value quantity in small prediction error, mean absolute error (MAE) method is better than the contrast and prediction time.
(3): in order to study the mobile ad hoc network environment in the non scoring service recommendation problem, this paper proposes a self organizing network in non score service recommendation model, proposes a calculation method of similarity between node service recommendation in self-organizing network. This paper argues that the similarity between nodes containing factors in two aspects, one is the objective factor of the mobile terminal, on the other hand is a node where the user's subjective factors. Based on the context information of nodes, this paper proposes a calculation method of node objectivity part similarity; at the same time, according to the information of user behavior, proposed a non similarity score subjective part of the node calculation method. Finally, through the experimental study of self organization network in various factors to influence the success rate of service recommendation.

【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2015
【分類號】：TP391.3

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本文編號：1406507