本文關(guān)鍵詞： 個性化推薦 協(xié)同過濾 情感分析 可解釋性 計算經(jīng)濟(jì)學(xué) 人工智能　出處：《清華大學(xué)》2016年博士論文　論文類型：學(xué)位論文

【摘要】：隨著互聯(lián)網(wǎng)的迅速發(fā)展,個性化推薦系統(tǒng)已經(jīng)逐漸成為各種網(wǎng)絡(luò)應(yīng)用中不可缺少的核心功能,并以各種各樣的方式影響著人們?nèi)粘Ｉ畹姆椒矫婷?電子商務(wù)網(wǎng)站中的購物推薦引擎為用戶提供可能感興趣的商品推薦;社交網(wǎng)絡(luò)中的好友推薦為用戶尋找潛在的好友關(guān)注;視頻網(wǎng)站中的視頻推薦為用戶提供最可能點擊的視頻推薦;新聞門戶網(wǎng)站中的內(nèi)容推薦為用戶提供最有信息量的新聞——個性化推薦技術(shù)已經(jīng)是支撐互聯(lián)網(wǎng)智能的基礎(chǔ)技術(shù)之一。個性化推薦系統(tǒng)已經(jīng)經(jīng)過了長達(dá)十幾年的研究和發(fā)展,然而隱變量方法的大量使用使得個性化推薦算法及其推薦結(jié)果的可解釋性仍然是困擾學(xué)術(shù)界重要問題之一,并且至今仍然沒有在產(chǎn)業(yè)應(yīng)用中得到很好的體現(xiàn)。舉例而言,在很多實際推薦系統(tǒng)中,算法只為用戶提供一份個性化的推薦列表作為結(jié)果,而難以向用戶解釋為什么要給出這樣的推薦。缺乏可解釋性的推薦降低了推薦結(jié)果的可信度,進(jìn)而影響推薦系統(tǒng)的實際應(yīng)用效果�？紤]到推薦系統(tǒng)的應(yīng)用范圍之廣和影響之大,可解釋性推薦的研究具有其重要性和緊迫性。在本文中,我們從數(shù)據(jù)、模型和經(jīng)濟(jì)意義三個方面對推薦系統(tǒng)的可解釋性進(jìn)行研究,主要有貢獻(xiàn)如下:1.數(shù)據(jù)的可解釋性:數(shù)據(jù)輸入是個性化推薦系統(tǒng)的第一步,而用戶物品評分矩陣是個性化推薦算法,尤其是基于矩陣分解的個性化推薦算法最主要的數(shù)據(jù)輸入形式。本文提出了基于雙邊塊對角矩陣的局部化矩陣分解框架,并將其應(yīng)用于矩陣分解的并行化。傳統(tǒng)的矩陣分解算法將原始矩陣看做一個整體進(jìn)行分解和預(yù)測,而缺乏對矩陣內(nèi)在結(jié)構(gòu)的理解。在本工作中,我們提出矩陣的雙邊塊對角結(jié)構(gòu),并在理論上證明該結(jié)構(gòu)與二部圖上社區(qū)發(fā)現(xiàn)算法的數(shù)學(xué)等價性,從而解釋矩陣內(nèi)在的社區(qū)結(jié)構(gòu)和社區(qū)關(guān)系。在社區(qū)結(jié)構(gòu)的基礎(chǔ)上,我們進(jìn)一步提出了局部化的矩陣分解框架,并理論證明了它與傳統(tǒng)矩陣分解算法的兼容性,從而為常用的矩陣分解算法提供了一個統(tǒng)一的并行化框架,在提高預(yù)測精度的同時大幅提高計算效率。2.模型的可解釋性:在用戶物品評分矩陣的數(shù)據(jù)基礎(chǔ)上,個性化推薦模型對用戶進(jìn)行偏好建模并給出個性化推薦。本文提出了基于短語級情感分析的顯式變量分解模型及其基于時間序列分析的動態(tài)化建模�；诰仃嚪纸獾碾[變量模型由于其較好的評分預(yù)測效果和可擴(kuò)展性,逐漸成為了個性化推薦的基礎(chǔ)算法并在實際系統(tǒng)中得到廣泛的應(yīng)用。然而由于變量本質(zhì)上的未知性,隱變量模型難以對推薦算法和推薦結(jié)果給出直觀可理解的解釋,進(jìn)而降低了推薦系統(tǒng)對用戶的可信度。在本工作中,我們利用短語級情感分析技術(shù)從大規(guī)模的用戶評論中抽取產(chǎn)品屬性詞及用戶在不同屬性上表達(dá)的情感,進(jìn)而引入顯式變量并提出基于顯式變量分解模型的個性化推薦算法,一方面使得模型的優(yōu)化過程具備了直觀意義,另一方面給出在模型層面可解釋的推薦結(jié)果和個性化推薦理由。由于用戶在不同屬性上的偏好具有間周期性,我們利用時間序列分析對用戶偏好進(jìn)行動態(tài)建模和預(yù)測,從而實現(xiàn)動態(tài)時間意義的可解釋性推薦。3.推薦的經(jīng)濟(jì)學(xué)解釋。推薦系統(tǒng)在用戶行為數(shù)據(jù)和個性化偏好建模的基礎(chǔ)上,以個性化推薦的方式隱式地調(diào)節(jié)商品在用戶中的匹配和購買,從而在最終層面上影響所屬系統(tǒng)的經(jīng)濟(jì)效益。本文提出基于互聯(lián)網(wǎng)系統(tǒng)總福利最大化的個性化推薦框架并給出典型應(yīng)用場景中的具體實現(xiàn)。隨著人類傳統(tǒng)線下活動的不斷線上化,常見的互聯(lián)網(wǎng)應(yīng)用均可以形式化為“生產(chǎn)者—服務(wù)—消費者”模型,例如在電子商務(wù)網(wǎng)站中,網(wǎng)絡(luò)商家(生產(chǎn)者)提供在線商品(服務(wù)),而網(wǎng)絡(luò)用戶(消費者)則在眾多的商品中進(jìn)行選擇和購買�；趥鹘y(tǒng)經(jīng)濟(jì)學(xué)的基本定義,本文首先給出了互聯(lián)網(wǎng)環(huán)境下效用、成本和福利的基本概念與統(tǒng)一形式,并進(jìn)一步給出了互聯(lián)網(wǎng)應(yīng)用中總社會福利的通用計算方法。在此基礎(chǔ)上,我們以互聯(lián)網(wǎng)服務(wù)分配為基本問題,提出基于網(wǎng)絡(luò)福利最大化的個性化推薦框架。進(jìn)一步,本文在典型的網(wǎng)絡(luò)應(yīng)用(電子商務(wù)、P2P借貸、在線眾包平臺)中對該框架進(jìn)行具體化,并進(jìn)行個性化的網(wǎng)絡(luò)服務(wù)推薦與評測。實驗結(jié)果表明,該方法可以在為用戶提供高質(zhì)量服務(wù)推薦的同時提升社會總福利,即在提升用戶體驗的同時又增強(qiáng)了社會效益。
[Abstract]:With the rapid development of Internet, personalized recommendation system has gradually become an indispensable core function of network application, and in a variety of ways affects all aspects of people's daily life: the e-commerce website in the shopping recommendation engine to provide users may be interested in recommendation; network of friends recommended for users to find the potential friends attention; video website video recommendation to provide users with the most likely to click on the video recommendation; in news portal content recommendation to provide users with the most informative news - personalized recommendation technology is the support of the Internet based intelligent technology. Personalized recommendation system has been the research and development of up to ten year, however the extensive use of latent variable methods make the personalized recommendation algorithm and recommendation results interpretation is still One of the most important problems in the academic circles, and are still not well reflected in the industrial application. For example, in many practical recommendation system. The algorithm only for the user to provide a list of recommended personalized to the user as a result, to explain why to give this recommendation. The lack of explanation can be recommended to reduce the recommendation of the credibility of the results, and then affect the recommendation system actual effect. Considering the application of recommendation system and wide influence, can explain the research recommendation has its importance and urgency. In this paper, from three aspects of our data model and the economic significance of recommender system research to explain, the main contributions are as follows: 1. the interpretability of data: data input is the first step of personalized recommendation system, and the user item rating matrix is a personalized recommendation algorithm, especially Is the data input form of personalized recommendation algorithm based on the matrix decomposition. This paper proposed a decomposition framework of localization matrix block diagonal matrix based on bilateral, parallel and its application in matrix decomposition. The traditional matrix decomposition algorithm for the original matrix as a whole decomposition and prediction, and the lack of the internal structure of the matrix understand. In this work, we propose a bilateral block diagonal structure of the matrix, and prove theoretically that mathematical equivalence of community discovery algorithm of the structure and the two plans, thus explaining the matrix inner community structure and community relations. Based on community structure, we further put forward the localization of the matrix decomposition framework. And it is proved in theory and the traditional matrix decomposition algorithm compatibility, thus provides a unified framework for parallelizing common matrix decomposition algorithm, to improve prediction precision At the same time of increasing the computing efficiency of the.2. model can explain: Based on user item rating matrix, personalized recommendation model of user preference modeling and personalized recommendation are put forward in this paper. The explicit variable analysis phrase level emotion model based on decomposition and dynamic modeling based on time series analysis of the latent variable model. Based on matrix decomposition due to its better prediction effect score and scalability, has gradually become the basis of personalized recommendation algorithm and is widely used in the actual system. However, due to the unknown variables in essence, latent variable model to the recommendation algorithm and recommendation results intuitively understandable explanation, thus reducing the recommendation the system reliability of the user. In this work, we use the phrase level sentiment analysis technology of extraction from large-scale user reviews of product attributes The expression of words and users in different attributes of emotion, and then introduce the explicit variable and proposed recommendation algorithm explicit variable decomposition model based on personalized, on the one hand makes the optimization process model has the intuitive meaning, recommendation results are given on the other hand can be explained in the model level and personalized recommendation reasons. Due to its periodic user preference in different attributes, we use time series analysis for dynamic modeling and prediction of user preferences, so as to realize the significance of dynamic time interpretability of the recommended recommended.3.. Economics recommendation system based on user behavior data and personal preference modeling, the personalized recommendation way implicitly to regulate commodity users the matching and purchase, thus affecting the economic efficiency of the system in the final level. In this paper, based on the total welfare maximization of the Internet system The specific implementation of the recommended framework is given in a typical scenario. With the traditional line activities constantly online, Internet applications are common can be formalized as "model of producer service consumers, for example in the electronic commerce website, online merchants (producers) to provide online commodity (service), and network the user (consumer) is to select and purchase in many commodities. Based on the basic definition of traditional economics, this paper first gives the Internet environment utility, the basic concept and the unified form of costs and benefits, and further gives a general method for calculating the total social welfare in Internet application. On this basis, we use the Internet service the distribution is the basic problem, put forward the recommended network welfare maximization based personalized framework. Further, based on the typical network application (e-commerce, online P2P lending. Crowdsourcing platform) in specific to the framework, and the network service recommendation and personalized evaluation. Experimental results show that this method can improve the total social welfare at the same time in providing high quality services for users to recommend, at the same time that enhance the user experience and enhance the social benefits.

【學(xué)位授予單位】：清華大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2016
【分類號】：TP391.3
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本文編號：1464807