本文選題：推薦算法 + SVD++��； 參考：《太原理工大學》2017年碩士論文

【摘要】：推薦系統(tǒng)是互聯(lián)網(wǎng)高速發(fā)展的產(chǎn)物,在人們的生活、工作及學習中發(fā)揮著非常重要的作用�，F(xiàn)如今,推薦系統(tǒng)已經(jīng)在電商、電影、社交等領(lǐng)域獲得飛速發(fā)展,國內(nèi)外針對推薦系統(tǒng)的應(yīng)用研究一直是近年的研究熱點。推薦算法及其所依賴的大數(shù)據(jù)是推薦系統(tǒng)的核心,基于SVD的推薦技術(shù)可以針對推薦系統(tǒng)中用戶-項目二元評分數(shù)據(jù)以及用戶-項目-標簽三元權(quán)值數(shù)據(jù)進行研究,是目前可以同時針對兩種數(shù)據(jù)進行處理的關(guān)鍵且有效的算法。但是隨著待處理信息的數(shù)據(jù)量不斷增大,算法計算效率和推薦準確性成為推薦系統(tǒng)研究的關(guān)鍵。本文針對SVD技術(shù)在推薦系統(tǒng)應(yīng)用中出現(xiàn)的計算效率低和推薦準確性不太理想的問題,分別對低階和高階SVD推薦算法性能進行了深入研究,本文所做的主要工作如下:1.首先,將基于SVD基本算法改進的LFM、Bias SVD和SVD++推薦算法的性能進行研究。其中LFM是將高維評分矩陣分解成兩個低維用戶和項目特征矩陣,Bias SVD算法是在LFM的基礎(chǔ)上將用戶和項目的基準信息加入模型,SVD++算法則是在Bias SVD算法之上又考慮了隱式信息。論文通過理論及實驗分別對三個模型的性能進行了比較,結(jié)果表明,SVD++算法的計算準確性最好,但是計算效率最低;LFM算法的計算效率最高,但是準確性最差。2.其次,針對SVD++算法計算復雜度偏高導致的計算效率低問題進行了深入研究。分析SVD++算法理論模型發(fā)現(xiàn),對預測模型目標函數(shù)的訓練采用梯度下降法開展時,所用學習率函數(shù)性能直接影響模型訓練所用迭代次數(shù)及收斂速度,因此本文提出了一種新學習率函數(shù)來對SVD++預測模型的特征參數(shù)進行學習,改進的學習率函數(shù)具有初始值大、中期下降迅速及后期值小并且緩慢變化的特點,實驗證明,此方法在采用梯度下降法對SVD++算法模型進行訓練的前提下,既能使SVD++推薦算法的計算效率明顯提高,又能保證預測準確性不變。3.最后,本文針對基于用戶-項目-標簽三元數(shù)據(jù)的HOSVD推薦算法進行研究。在推薦系統(tǒng)里,用戶-項目-標簽數(shù)據(jù)會經(jīng)常出現(xiàn)標簽冗余現(xiàn)象,若能充分利用該特點,尋找標簽與標簽之間的關(guān)聯(lián)性,對進一步提高預測效率非常有益。為此,本文提出了一種基于Apriori算法重組標簽的HOSVD推薦算法,首先采用Apriori算法對原始標簽數(shù)據(jù)進行預處理,尋找標簽頻繁項集,設(shè)定為新標簽,并對標簽進行編號,組成新的用戶-項目-標簽數(shù)據(jù),再利用HOSVD算法對新組成的數(shù)據(jù)進行計算處理。通過實驗,本文方法的推薦性能有了明顯提高。
[Abstract]:Recommendation system is the product of the rapid development of the Internet. It plays a very important role in people's life, work and study. Nowadays, recommendation system has been developing rapidly in the fields of e-commerce, film, social interaction, etc. The research on the application of recommendation system at home and abroad has been a hot topic in recent years. The recommendation algorithm and its dependent big data are the core of the recommendation system. The recommendation technology based on SVD can be used to study the user-item binary score data and the user-project-label ternary weight data in the recommendation system. It is a key and effective algorithm which can deal with two kinds of data at the same time. However, with the increasing of the amount of information to be processed, the computational efficiency and recommendation accuracy of the algorithm become the key to the research of recommendation system. Aiming at the problems of low computing efficiency and low recommendation accuracy in the application of SVD technology in recommendation system, the performance of low-order and high-order SVD recommendation algorithms are studied in this paper. The main work of this paper is as follows: 1. Firstly, the performance of the improved LFM SVD Bias SVD and SVD recommendation algorithm is studied. LFM decomposes the high-dimensional scoring matrix into two low-dimensional users and the item feature matrix Bias SVD algorithm. On the basis of LFM, the benchmark information of users and items is added to the model. The algorithm is based on the Bias SVD algorithm and the implicit information is taken into account. The performance of the three models is compared in theory and experiment. The results show that the SVD algorithm has the best accuracy, but the LFM algorithm has the lowest computational efficiency, but the accuracy is the worst. Secondly, the problem of low computational efficiency caused by high computational complexity of SVD algorithm is studied in depth. By analyzing the theoretical model of SVD algorithm, it is found that the performance of the learning rate function directly affects the iterative times and convergence speed of the training of the model when the training of the objective function of the prediction model is carried out by gradient descent method. In this paper, a new learning rate function is proposed to study the characteristic parameters of the SVD prediction model. The improved learning rate function has the characteristics of large initial value, rapid decline in the middle period and small and slow change in the later period. On the premise of using gradient descent method to train the model of SVD algorithm, this method can not only improve the calculation efficiency of SVD recommendation algorithm, but also ensure the accuracy of prediction. 3. Finally, this paper studies the HOSVD recommendation algorithm based on user-item-tag ternary data. In the recommendation system, user-item-label data often appear label redundancy phenomenon. If we can make full use of this feature and find the correlation between label and label, it is very helpful to improve prediction efficiency. For this reason, this paper proposes a HOSVD recommendation algorithm based on Apriori algorithm to reorganize the label. Firstly, the Apriori algorithm is used to preprocess the original label data, to find the tag frequent itemsets, to set the label as a new label, and to number the label. The new user-item-label data is formed, and the newly formed data is calculated and processed by HOSVD algorithm. Through experiments, the recommended performance of this method has been improved obviously.
【學位授予單位】：太原理工大學
【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：1889053