本文選題：推薦系統(tǒng) + 矩陣分解��； 參考：《江蘇大學》2017年碩士論文

【摘要】：計算機的迅速發(fā)展帶來了嚴重的信息過載問題,無形之中增加了用戶獲取自己想要的信息的難度,個性化推薦系統(tǒng)正是在這樣的情況下產生了。個性化推薦系統(tǒng)將用戶的歷史交互行為記錄下來并進行詳細地分析,基于這些分析的結果為用戶推薦他們可能感興趣的商品。個性化推薦系統(tǒng)不僅為用戶帶來了較好的用戶體驗,同時也為網站本身提供了個性化決策機制。本文介紹了個性化推薦系統(tǒng)中所涉及的相關概念、經典的推薦算法、推薦系統(tǒng)的應用場景等,重點研究了二維矩陣分解推薦算法和高維張量分解推薦算法。論文就這些算法存在的數(shù)據(jù)稀疏性和冷啟動問題提出了相應的改進方案。本文的主要工作如下:(1)針對傳統(tǒng)的矩陣分解協(xié)同過濾算法仍然存在的數(shù)據(jù)稀疏性問題,提出融合社交信息的矩陣分解推薦模型。通常發(fā)現(xiàn)身邊的好友的建議會潛移默化地影響我們的購買行為,用戶的購買行為不僅跟自己的興趣相關,同時還會受到他所信任的好友的影響。本文在傳統(tǒng)SVD分解模型的基礎上,一方面考慮了用戶和項目的固有屬性對評分的影響,另一方面利用社交網絡中的好友關系修正矩陣分解模型,然后使用隨機梯度下降法進行矩陣分解。實驗結果表明,優(yōu)化后的算法在實際應用中比傳統(tǒng)的SVD推薦算法具有更好的推薦效果。(2)針對基于張量分解的推薦算法存在推薦精度上的問題,提出融合用戶近鄰信息的N維張量分解算法。首先引入上下文感知信息,把上下文感知中的隱式反饋信息作為張量的第三維度,來建立N維張量分解模型;同時為了進一步提高推薦質量,引入用戶近鄰信息來優(yōu)化N維張量分解模型,提高了張量分解推薦算法的準確率。實驗結果表明:融合用戶近鄰的張量分解推薦算法比傳統(tǒng)的張量分解算法具有更好的準確性,能有效解決稀疏性和準確性問題。(3)將本文提出的融合社交信息的矩陣分解推薦模型以及融合用戶近鄰信息的N維張量分解算法應用到軟裝電子商務系統(tǒng)中,介紹了推薦系統(tǒng)的總體架構和技術選型。該電商系統(tǒng)為了適應多種推薦需求,還使用到了經典的推薦算法。該系統(tǒng)從架構上主要分為數(shù)據(jù)層、推薦算法層、應用接口層、應用層。該系統(tǒng)的推薦模塊主要包括“看了又看”、“買了又買”、“猜你喜歡”、“搭配推薦”等。推薦系統(tǒng)為該電商系統(tǒng)不僅帶來了更好的用戶體驗,同時也吸引了許多的用戶。
[Abstract]:The rapid development of computer has brought serious information overload problem, which increases the difficulty for users to obtain the information they want. The personalized recommendation system records the user's historical interaction behavior and analyzes it in detail. Based on the results of these analyses, the users can be recommended for the products they may be interested in. Personalized recommendation system not only brings users a better user experience, but also provides a personalized decision-making mechanism for the website itself. This paper introduces the concepts involved in the personalized recommendation system, the classical recommendation algorithm, the application scenario of the recommendation system, and focuses on the two-dimensional matrix decomposition recommendation algorithm and the high-dimensional Zhang Liang decomposition recommendation algorithm. In this paper, the data sparsity and cold start problem of these algorithms are improved. The main work of this paper is as follows: (1) aiming at the problem of data sparsity still existing in the traditional matrix decomposition and collaborative filtering algorithm, a matrix decomposition recommendation model is proposed to fuse social information. It is often found that the advice of friends around us will affect our purchasing behavior. The user's purchase behavior is not only related to their own interests, but also influenced by their trusted friends. On the basis of the traditional SVD decomposition model, on the one hand, we consider the influence of the inherent attributes of users and items on the score, on the other hand, we use the friend relationship in social network to modify the matrix decomposition model. Then the stochastic gradient descent method is used to decompose the matrix. The experimental results show that the optimized algorithm has better recommendation effect than the traditional SVD recommendation algorithm in practical application. (2) the recommendation algorithm based on Zhang Liang decomposition has the problem of recommendation accuracy. This paper presents a N-dimensional Zhang Liang decomposition algorithm which combines user's nearest neighbor information. Firstly, the context-aware information is introduced, and the implicit feedback information in context-aware is regarded as the third dimension of Zhang Liang to establish the N-dimensional Zhang Liang decomposition model. The user nearest neighbor information is introduced to optimize the N-dimensional Zhang Liang decomposition model, which improves the accuracy of the Zhang Liang decomposition recommendation algorithm. The experimental results show that the proposed Zhang Liang decomposition recommendation algorithm is more accurate than the traditional Zhang Liang decomposition algorithm. It can effectively solve the problem of sparsity and accuracy. It applies the matrix decomposition recommendation model of fusion of social information and the N-dimensional Zhang Liang decomposition algorithm of user's nearest neighbor information to the soft electronic commerce system. The general structure and technology selection of recommendation system are introduced. In order to meet the needs of many kinds of recommendation, the system also uses the classical recommendation algorithm. The system is divided into data layer, recommendation algorithm layer, application interface layer and application layer. The recommendation module of the system mainly includes "read and see", "buy and buy", "guess you like", "match recommendation" and so on. The recommendation system not only brings a better user experience, but also attracts many users.
【學位授予單位】：江蘇大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.3

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本文編號：1901987