本文選題：多屬性　切入點：推薦系統(tǒng)　出處：《江西財經大學》2016年碩士論文

【摘要】：在21世紀的今天,信息爆炸的時代里,每個人面對的信息已經數(shù)以億計了,尤其是在電子商務網(wǎng)站,用戶如何找到自己感興趣的信息,已經不單單局限于自己去尋找,更需要電子商務推薦系統(tǒng)幫助用戶發(fā)現(xiàn)他感興趣的信息。所以推薦系統(tǒng)的研究領域變得越來越重要,它可以為用戶推薦最感興趣的信息。目前,大部分的推薦系統(tǒng)是通過用戶對產品的評價信息進行個性化推薦的,這些顯式或隱式的評價信息被表示成用戶對被評分項目在單一維度上的偏好等級,這種單一維度上的評分信息不能有效表達用戶對某個產品的各個方面的偏好程度的差異性,進而影響推薦算法的推薦性能。針對傳統(tǒng)基于單一維度評分的推薦算法的不足,基于多屬性評分的推薦系統(tǒng)考慮用戶對產品各個方面評價信息的差異進行個性化推薦決策,本文的主要工作包括如下幾個方面。首先,在考慮酒店的多個屬性評分的基礎上,對改進的基于多屬性評分的協(xié)同過濾推薦算法的三種方法進行實證分析比較它們的準確性和多樣性,這三種方法分別是:基于多屬性相似性平均協(xié)同過濾、基于多維距離的協(xié)同過濾算法和基于層次分析法(AHP)協(xié)同過濾算法。其次,再分析酒店多維度評分信息后,把線性規(guī)劃模型引入到求各個屬性的權重中來,提出了基于多屬性線性規(guī)劃的協(xié)同過濾算法。再次,如何衡量一個推薦系統(tǒng)好壞的指標有很多,本文主要探討比較準確性和多樣性這兩個常用的評價指標,雖然推薦準確度無疑是重要的,但學者日益認識到更高的準確性并不總是意味著對用戶有用,也許用戶希望所推薦的商品具有多樣性。因此,除了分析準確度之外,本論文還考慮了另一個重要的指標多樣性來衡量推薦的質量,并探討了準確性和多樣性之間的關系。最后,為了實證和驗證所提出的方法,我們通過在酒店網(wǎng)站收集真實用戶數(shù)據(jù)。收集到了 165829位用戶對酒店的多屬性評分(分別是性價比評分、舒適度評分、位置評分、衛(wèi)生評分、睡眠評分、服務評分)。平均絕對誤差(MAE)以及多樣性被用來衡量算法表現(xiàn)。實驗結果顯示我們提出的改進方法在多屬性環(huán)境下可以顯著的提高推薦準確性和用戶多樣性。
[Abstract]:Today in the 21st century, in the era of information explosion, everyone has faced hundreds of millions of information, especially in e-commerce sites, how users find their own interested in information, is not limited to their own search,E-commerce recommendation system is needed to help the user to find the information he is interested in.Therefore, the research field of recommendation system is becoming more and more important, it can recommend the most interesting information for users.At present, most recommendation systems make personalized recommendation through users' evaluation information of products. These explicit or implicit evaluation information are expressed as the user's preference level on a single dimension for the rated items.This kind of rating information on a single dimension can not effectively express the difference of the user's preference degree to each aspect of a product, and then affect the recommendation performance of the recommendation algorithm.In view of the shortcomings of the traditional recommendation algorithm based on single dimension rating, the recommendation system based on multi-attribute scoring takes into account the users' differences in evaluation information in all aspects of the product. The main work of this paper includes the following aspects.First of all, on the basis of considering the multiple attributes of the hotel, three improved collaborative filtering recommendation algorithms based on multi-attribute scoring are analyzed and compared with each other in terms of their accuracy and diversity.The three methods are: average collaborative filtering based on multi-attribute similarity, collaborative filtering algorithm based on multi-dimension distance and collaborative filtering algorithm based on analytic hierarchy process (AHP).Secondly, after analyzing the multi-dimension rating information of hotel, the linear programming model is introduced to calculate the weight of each attribute, and a collaborative filtering algorithm based on multi-attribute linear programming is proposed.Thirdly, there are a lot of indicators to measure the quality of a recommendation system. This paper mainly discusses the two commonly used evaluation indicators, namely, accuracy and diversity, although recommendation accuracy is undoubtedly important.But scholars increasingly realize that higher accuracy does not always mean that it is useful to users, who may want a variety of products to recommend.Therefore, in addition to analytical accuracy, this paper also considers another important indicator diversity to measure the quality of recommendations, and discusses the relationship between accuracy and diversity.Finally, in order to demonstrate and verify the proposed method, we collect real user data on the hotel website.A total of 165829 users rated the hotel for multiple attributes (performance-to-price score, comfort score, location score, health score, sleep score, service score, etc.).The mean absolute error (mae) and diversity are used to measure the performance of the algorithm.The experimental results show that the proposed method can significantly improve the accuracy of recommendation and user diversity in multi-attribute environment.
【學位授予單位】：江西財經大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：F724.6

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